Automated diagnostic system and method including encoding patient data

ABSTRACT

Structure-based processing includes a method of diagnosing diseases that works by arranging diseases, symptoms, and questions into a set of related disease, symptom, and question structures, such as objects or lists, in such a way that the structures can be processed to generate a dialogue with a patient. A structure-based processing system organizes medical knowledge into formal structures and then executes those structures on a structure engine to automatically select the next question. Patient responses to the questions lead to more questions and ultimately to a diagnosis. An object-oriented embodiment includes software objects utilized as active, intelligent agents where each object performs its own tasks and calls upon other objects to perform their tasks at the appropriate time to arrive at a diagnosis. Alternative symptoms, synergies, encoding of patient responses, multiple diagnostic modes, disease profiles or timelines, and the reuse of diagnostic objects enhance the processing of the system and method.

PRIORITY

This application is a continuation of application Ser. No. 09/785,045,entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHOD INCLUDING ENCODINGPATIENT DATA”, filed Feb. 14, 2001 now U.S. Pat. No. 6,475,143. Thebenefit under 35 U.S.C. §119(e) of U.S. provisional application,Application No. 60/182,176, filed Feb. 14, 2000, entitled “AUTOMATEDDIAGNOSTIC SYSTEM AND METHOD” is hereby claimed.

RELATED APPLICATIONS

The subject matter of U.S. patent applications: application Ser. No.09/785,047, filed Feb. 14, 2001 and entitled “AUTOMATED DIAGNOSTICSYSTEM AND METHOD INCLUDING ALTERNATIVE SYMPTOMS”; application Ser. No.09/785,037, filed Feb. 14, 2001 and entitled “AUTOMATED DIAGNOSTICSYSTEM AND METHOD INCLUDING SYNERGIES”; application Ser. No. 09/785,043,filed Feb. 14, 2001 and entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHODINCLUDING MULTIPLE DIAGNOSTIC MODES”; application Ser. No. 09/785,046,filed Feb. 14, 2001 and entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHODINCLUDING DISEASE TIMELINE”; and application Ser. No. 09/785,044, filedFeb. 14, 2001 and entitled “AUTOMATED DIAGNOSTIC SYSTEM AND METHODINCLUDING REUSE OF DIAGNOSTIC OBJECTS” are related to this application.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention relates to computerized medical diagnosticsystems. More particularly, embodiments of the present invention relateto a computerized system for time-based diagnosis of a patient's medicalcomplaint by use of dynamic data structures.

2. Description of the Related Technology

Health care costs currently represent a significant portion of theUnited States Gross National Product and are generally rising fasterthan any other component of the Consumer Price Index. Moreover, usuallybecause of an inability to pay for medical services, many people aredeprived of access to even the most basic medical care and information.

Many people delay in obtaining, or are prevented from seeking, medicalattention because of cost, time constraints, or inconvenience. If thepublic had universal, unrestricted, and easy access to medicalinformation, many diseases could be prevented. Likewise, the earlydetection and treatment of numerous diseases could keep many patientsfrom reaching the advanced stages of illness, the treatment of which isa significant part of the financial burden attributed to our nation'shealth care system. It is clear that the United States is facinghealth-related issues of enormous proportions and that present solutionsare not robust.

Previous attempts at tackling the healthcare problem have involvedvarious forms of automation. Some of these attempts have been in theform of a dial-in library of answers to medical questions. Otherattempts have targeted providing doctors with computerized aids for useduring a patient examination. These methods involve static procedures oralgorithms. What is desired is an automated way of providing to apatient medical advice and diagnosis that is quick, efficient andaccurate. Such a medical advice system should be modular to allowexpansion for new types of medical problems or methods of detection.

SUMMARY OF THE INVENTION

Structure-based processing is a method of diagnosing diseases that worksby arranging diseases, symptoms, and questions into a set of relateddisease, symptom, and question structures, such as objects or lists, insuch a way that the structures can be processed to generate a dialoguewith a patient. Each question to the patient generates one of a set ofdefined responses, and each response generates one of a set of definedquestions. This establishes a dialogue that elicits symptoms from thepatient. The symptoms are processed and weighted to rule diseases in orout. The set of ruled-in diseases establishes the diagnosis. Astructure-based processing system organizes medical knowledge intoformal structures and then executes those structures on a structureengine, such as a list-based engine, to automatically select the nextquestion. The responses to the questions lead to more questions andultimately to a diagnosis.

An additional aspect of the invention includes a computerized medicaldiagnosis system, comprising a) means for defining a spectrum of termsrepresentative of a subjective description for an aspect of a medicalsymptom; b) means for presenting the spectrum of terms to a patientduring a diagnosis session; c) means for selecting a term from among thespectrum of terms; d) means for repeating a)-c) for other aspects of themedical symptom; e) means for encoding the selected terms; and f) meansfor indexing a database of diseases with the encoded terms therebydiagnosing a disease.

An additional aspect of the invention includes a computerized medicaldiagnosis system, comprising a) means for defining a spectrum of termsrepresentative of a subjective description for a selected aspect of amedical symptom; b) means for defining diagnostic weights for each termof the spectrum; c) means for presenting the spectrum of terms to apatient during a diagnostic consultation; d) means for receiving aselected term from among the spectrum of terms; e) means forcorresponding the selected term to a weight; f) means for applying theweight corresponding to the selected term to a diagnostic score so as todiagnose a medical condition; g) means for repeating the acts a)-f) forother aspects of the medical symptom so as to receive other selectedterms; and h) means for encoding the selected terms into a code.

An additional aspect of the invention includes a method of developing adata structure of aspects of a medical symptom for use in diagnosis ofmedical conditions, the method comprising identifying a plurality ofaspects of a particular medical symptom; defining a spectrum of termsfor each aspect of the plurality of aspects; assigning a correspondingdiagnostic weight for each term of each spectrum of terms; and storingthe aspects, the spectrum of terms for each aspect and the correspondingdiagnostic weights in a data structure for use in diagnosis of diseasesassociated with the medical symptom.

Yet an additional aspect of the invention includes a computerizedmedical diagnosis system, comprising a computerized device; a datastructure for a spectrum of terms representative of a subjectivedescription for each of a plurality of selected aspects of a medicalsymptom, wherein each aspect has a different spectrum of terms andwherein each term of the spectrum has a corresponding diagnostic weight;and a diagnosis program configured, during a diagnostic consultation,to: a) present a spectrum of terms for one aspect of the medical symptomto a patient, b) receive a selected term from among the spectrum ofterms, c) apply a weight from the data structure corresponding to theselected term to a diagnostic score so as to diagnose a medicalcondition, d) repeat a)-c) for other aspects of the medical symptom soas to receive other selected terms, and e) encode the selected termsinto a code.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of one embodiment of a diagnostic loop performedby a structure-based engine of a medical diagnostic and treatment advicesystem.

FIG. 2 is a flowchart of the Set Up Diagnostic Loop function shown inFIG. 1.

FIG. 3 is a flowchart of the Set Up Disease-Symptom Structure functionshown in FIG. 2.

FIG. 4 is a flowchart of the Pick Current Disease function shown in FIG.1.

FIG. 5 is a flowchart of the Pick Current Symptom function shown in FIG.1.

FIG. 6 is a flowchart of the Obtain Symptom Value function shown in FIG.1.

FIG. 7 is a flowchart of the Use Question Valuator Object function shownin FIG. 6.

FIG. 8 is a flowchart of the Use Formula Valuator Object function shownin FIG. 6.

FIG. 9 is a flowchart of the Use Lookup Valuator Object function shownin FIG. 6.

FIG. 10 is a flowchart of the Use Spectrum of Terms Valuator Objectfunction shown in FIG. 6.

FIG. 11 is a flowchart of the Apply Symptom Value function shown in FIG.1.

FIG. 12 is a flowchart of the Compute Synergies function shown in FIG.11.

FIG. 13 is a flowchart of the Calculate FSS Synergy function shown inFIG. 12.

FIG. 14 is a flowchart of the Calculate Onset [Offset] Synergy functionshown in FIG. 12.

FIG. 15 is a flowchart of the Analyze Onset [Offset] Synergy functionshown in FIG. 14.

FIG. 16 is a flowchart of the Compute Onset [Offset] Slope functionshown in FIG. 15.

FIG. 17 is a flowchart of the Compute Onset [Offset] Trend functionshown in FIG. 15.

FIG. 18 is a flowchart of the Calculate Sequencing Synergy functionshown in FIG. 12.

FIG. 19 is a flowchart of the Calculate Simultaneous Synergy functionshown in FIG. 12.

FIG. 20 is a flowchart of the Calculate Time Profile Synergy functionshown in FIG. 12.

FIG. 21 is a flowchart of the Update and Record function shown in FIG.1.

FIG. 22 is a flowchart of the Review Diagnoses function shown in FIG. 1.

FIG. 23 is a flowchart of the Review Diagnostic Goals function shown inFIG. 22.

FIG. 24 is a flowchart of the Shut Down Diagnostic Loop function shownin FIG. 1.

FIG. 25 is a flowchart of the Symptom Slope function shown in FIGS. 16and 26.

FIG. 26 is a flowchart of the Symptom Trend function shown in FIG. 17.

FIG. 27 is a diagram of an exemplary disease-symptom matrix.

FIG. 28 is a diagram of an exemplary generic disease timeline.

FIG. 29a is a diagram of one configuration of objects used by anembodiment of the medical diagnostic and treatment advice system.

FIG. 29b is a diagram of an exemplary on-line use of the configurationof objects shown in FIG. 29a to develop a diagnosis.

FIG. 30 is a flowchart of an alternative symptom weights feature of themedical diagnostic and treatment advice system.

FIG. 31 is a flowchart of a reuse of medical objects feature of themedical diagnostic and treatment advice system.

FIG. 32a is a flowchart of a setup of symptom elements aspect of themedical diagnostic and treatment advice system.

FIG. 32b is a flowchart of a mode switching feature using the symptomelements of FIG. 32a.

FIG. 33 is a flowchart of one embodiment of a disease timelines aspectof the medical diagnostic and treatment advice system.

FIG. 34 is a flowchart of another embodiment of a disease timelinesaspect of the medical diagnostic and treatment advice system.

FIG. 35 is a block diagram illustrating components of one embodiment ofthe computerized medical diagnostic and treatment advice (MDATA) systemof the present invention.

DETAILED DESCRIPTION

The following detailed description presents a description of certainspecific embodiments of the present invention. However, the presentinvention may be embodied in a multitude of different ways as definedand covered by the claims. In this description, reference is made to thedrawings wherein like parts are designated with like numeralsthroughout.

The detailed description will begin with an overview of the specificembodiments followed by a description of each of the drawings. Theoverview is partitioned into the following sections: Terminology,Object-based Medical Diagnosis, Object-based Method, Disease Object,Symptom Object, Valuator Object, Question Object, Node Object,List-based Engine Concepts, Dynamic Rules and Goals, Dynamic Momentum,Horizontal Axis of Inquiry (HAI), Vertical Axis of Inquiry (VAI),Alternative Symptoms, Disease Timeline, Spectrum of Terms/PQRST Code,and Synergy.

I. Terminology

The terms presented in this section include text to assist inunderstanding their meanings. Nonetheless, nothing in this section ismeant to limit the meanings attributed to each word.

Patient

At some point in everyone's life, there are situations that create areal health problem, one that requires medical attention and treatment.The health problem may, for example, be caused externally, by slippingon a bar of soap in the shower, being scratched by a cat, lifting achild in an awkward manner, inhaling some airborne bacteria, being stungby a mosquito that carries malaria, or getting into a traffic accident.Or the problem may arise internally, by some tiny tube clogging up, someorgan being overloaded, some tissue degenerating with age, some anatomicsystem getting too much of one chemical and not enough of another, orsome cells deciding to grow unchecked into a cyst or a tumor, forinstance. The person with the health problem is called the patient, inorder to distinguish him or her from other persons involved in the case,such as the patient's friends and relatives, doctors and nurses,therapists and pharmacists, lawyers and insurance agents and HMOs.

Disease

There are many words for the general concept of someone being sick. Apatient may be said to have an abnormality, affliction, ailment,anomaly, cause, complaint, condition, disease, disorder, illness,indisposition, infirmity, malady, problem, or sickness. We use the worddisease. Some diseases, such as pregnancy, can actually be joyous newsto the patient.

Symptom

As the disease progresses and evolves in the patient, it creates variousdirect and indirect effects that can be noticed by the patient orexternally observed or measured. These signs of disease also havevarious names in medicine, such as complaint, effect, indication,manifestation, presentation, problem, sign, or symptom. We use the wordsymptom.

Doctor

A person with symptoms, i.e., a patient, typically seeks help fromsomeone trained in medicine, who may be any one of the following:attendant, clinician, dentist, doctor, expert, healthcarer, MD, medic,midwife, nurse, ophthalmologist, optician, paramedic, physician,practitioner, professor, provider, psychiatrist, specialist, surgeon, ortechnician. For our purposes, we use the word doctor in its most generalsense of someone who is trained and experienced in at least some aspectof medicine, as opposed to the general lay public that has no formalmedical training.

Examination

In real-world medicine, the doctor usually examines the patient todetermine the extent of the symptoms and to make other observations thatthe patient is not trained to notice. In the automated medical world ofthe present diagnostic system, physical examination is necessarilysecondary, since the patient is always remote from the “doctor”. Onepartial solution is to train the patient (or an associate) to performself-examination at home, perhaps with the aid of an examination kit.Another partial solution is to refer the patient to a doctor or to a labfor a specified examination or test. This is often done in real-worldmedicine, when—for example—a doctor refers a patient to a specialist, orsends the patient to a lab for a special test or examination.

Laboratory Test

Some symptoms can only be measured by special chemical, electronic, ormechanical devices which require a specially equipped laboratory andtrained lab technicians. Such labs typically obtain a sample of thepatient's body fluids or tissue, called a specimen. They analyze thespecimen and report the results to the doctor. A key to understandinglab tests is that one must ask the lab what test to perform. Contrary topublic opinion, a lab does not just test “for everything.” There areperhaps 1500 different lab tests, and the lab will perform only what isrequested. So it is important for the doctor to determine which test torequest, and this, in turn, depends on the diagnosis.

With new monoclonal antibody and polymerase chain reactions (PCR) tests,an ever-increasing number of laboratory tests can be performed by thepatient or an assistant at home. Examples would be urine pregnancytests, a urine dipstick to detect leukocyte esterase, and nitrates todiagnose a urinary tract infection. Diabetics prick their skin to get asmall amount of blood so that a home glucometer can determine theirblood sugar level and, thus, how much insulin to take.

A home diagnostic kit is available that provides the most currenttechnologies, and may be referenced during consultations with thepatient if the patient has one.

Imaging Modality

Some symptoms can be observed by devices that display some part of thebody as an image. The best known of these is the x-ray. Others areultrasound, CAT scan, MRI scan and PET scan. Imaging modalitiestypically require the presence of the patient to take a “picture” ofsome part or all of the body. The imaging lab takes the picture and mayhave a resident specialist who interprets the image for the doctor. Inany case, the results of the imaging study are forwarded to the doctor.

Session

In the automated method utilized by the present diagnostic system, thepatient contacts the system by phone, the Internet or some othercommunication mechanism. The patient interfaces with the system, so thatthe system plays the role of the doctor, and no human doctor isinvolved. One such consultation may be called a session.

Question

In the automated approach used by the present diagnostic system, most ofthe information about a health problem is obtained during a session, byasking the patient (or someone speaking for the patient) questions.Asking a question typically involves a number of elements such asintroducing a topic, giving background, defining terms, suggestingapproximations, asking the actual question, and instructing the patienthow to indicate the response (“press 1 for yes; press 2 for no”). Thus,question generally includes all of these elements. When reference ismade to the actual question, the term question text may be used.

Valuation

In addition to questions, health data can be obtained by variouscomputational algorithms such as sorting and searching, comparing andmatching, mathematical or graphical calculations, logical inferences, orlooking up data in tables and databases. In the automated method, theword valuation may be used for all computations of health data that donot involve the patient. A simple example is to classify the patient'sage into labels used in diagnoses, such as Newborn, Infant, Child,Teenager, Adult, Senior, and so on. Once the system obtains thepatient's age from the patient, it does not need to ask the patientwhether he or she is a teenager; this can be done internally, usingvaluator objects.

Diagnosis

In real-world medicine, after the doctor has accumulated the necessaryhealth data from and about the patient, the doctor makes a diagnosis,which means that the doctor identifies the patient's sickness for thepurpose of treatment. For every chief complaint, the doctor knows adifferential diagnosis, which is a relatively short list of possiblediagnoses. After the doctor has accumulated the necessary health datafrom and about the patient, the doctor has at the least, a patientspecific differential diagnosis, and hopefully “the” diagnosis. In theautomated method of the diagnostic system, the software establishes the(differential) diagnosis by comparing the patient's symptoms to itsdatabase of diseases and symptoms.

Treatment

When the doctor has established the diagnosis, the doctor can take stepsto heal the patient. As always, there are many words for this such asadvice, counseling, first aid, health care, healing, intervention,medication, nursing, prescription, rehabilitation, surgery, therapy, andtreatment. For all of these, this document may use the word treatment.

Disease Management

Some diseases may require continuing treatment and repeated examinationsfor months or years, or even for the patient's lifetime. Such long-termtreatment is called disease management.

Object

In computer software terms, an object is combination of data andprocesses that manipulate the data. The data are said to be“encapsulated,” meaning that they are hidden, so that a user of theobject only sees processes that can be invoked. Using an object'sprocesses, one can then manipulate the data without having to know theexact location and format of the data. When more than one copy of theobject is required, one can make copies of the data, but use the sameprocess set to manipulate each of the copies as needed. This set ofprocesses can then be thought of as an “engine” that controls orrepresents the objects' behavior, whether there are 10 or 10,000 objectcopies.

II. Object-Based Medical Diagnosis

This section describes a new diagnostic paradigm that uses softwareobjects to establish a broad, generalized software environment formedical diagnosis, which is used to define and develop the programmingelements of medical diagnosis. The objects are then used to guide andcontrol the diagnostic process, to conduct the patient interview, toperform related analytical tasks, and to generate the diagnoses. Asoftware object is a fundamental software structure that can be used toorganize the processes and data of a computer program in such a way asto make possible very complicated applications. This description willdiscuss novel uses of object oriented programming (OOP) in medicaldiagnosis, such as the use of software objects for the purpose of fullyautomated medical diagnosis, the entire/overall method of dynamicallyassembling the components of diagnosis in the form of objects, and thenletting the objects interact to compute a diagnosis.

Defining and creating software objects is well-known to any programmertrained in object-oriented programming. Using an OOP-capable compiler,the programmer defines the data that represent the object and theactions that the object can perform. At run time, the program creates anobject, supplies the data that define the object, and then manipulatesthe object using the object actions. The program can create any numberof objects as needed. Each object can be independently initialized,manipulated, and destroyed.

III. The Object-Based Method

In the object-based (OB) method discussed here, software objects arcused as active, intelligent agents that represent all of the players andall of the data in suitably organized roles. It is important to note inthis metaphor that all of the disease objects, which are “specialists”for a single disease, are allowed to monitor the questions and answersof other objects.

One key concept of the OB method is to think of disease and symptomobjects as representing the medical experts inside the computer. If weask the Appendicitis Disease Object to look at a patient, the objectlooks at the patient data, notes that the patient does indeed complainof abdominal pain and nausea—but then “notices” the appendectomy scar!Obviously, appendicitis can be ruled out,; but instead of shrugging itsshoulders and giving up, the Appendicitis Disease Object now invokesanother disease object that is an expert in, say, Small BowelObstruction. That object takes a look, asks some questions, and passesthe patient on to still other disease objects. In effect, a huge numberof diagnostic experts are gathered at the patient's bedside, and eachobject gets a turn at evaluating the patient data in terms of its ownsymptom pattern.

As an actual patient symptom set is built up, disease objects judgethemselves and judge the likelihood of other diseases. The emergenteffect is a patient interview and a diagnostic evaluation that—bydesign—constantly stays focused on the most likely set of diseases ofthe patient. Carefully focused questions are used to eliminate or reducethe likelihood of diseases, to promote others into the “realm ofsuspicion,” and to expand the search in a promising direction, based onthe data being obtained from the patient.

Object Overview

A software “object” is basically a data structure plus associatedprocesses that can do things with or for or to the data. An importantproperty of an object is that the object's data can be hidden behind theobject's processes, so that the outside user of the object can only seeand use object processes that can be invoked to access the data. Theobject is said to “hide” data; it provides the powerful ability ofdecoupling the world that uses an object from the object itself.

Now assume an object is a “smart doctor”. Not one that knows everythingabout medicine, but just one that knows about, say, “Malaria Vivax inimmunocompetant female of child-bearing age that is resident of UpperGabon”. This object knows nothing about anything other than everythingabout one tiny portion of one specific disease. Next, this diseaseobject is trained to diagnose a patient. It is free to access thepatient's medical record, to ask the patient questions, to ask forcertain lab tests, and to compare the patient to other patients in thesame household or region (e.g., to detect infection or epidemic). Intrue OOP fashion, the disease object does not actually ask the patient,but it calls a Symptom Object which calls a Question Object, which usesa Node Object, which interfaces with a Patient Object, which interfaceswith the Communication Object which interfaces with the Port Object andso on. One of the objects, somewhere down the hierarchy actuallydisplays a message on a screen, or speaks a question into a telephonemodem, or sends a question on a fax machine.

Suppose thousands of disease objects have been defined, each with awell-defined, different, specific disease in mind. One disease object isnot necessarily matched with one disease, but divide each disease intoits major phases. Define Appendicitis as, say, three disease objects:(1) early, pre-RLQ-Pain Appendicitis, (2) middle Appendicitis, from RLQpain to Rupture, and (3) late, post-rupture Appendicitis. Let thesethree objects interview a patient and compete with each other as to thecondition of the patient.

Now define thousands of symptom objects, each for a different specificsymptom. Again, divide complex symptoms into less complex ones, so thatthey build on each other. Define Cough into, say, 12 types that patientscan identify and doctors can use to diagnose. Define fever into usefullevels. Define pain into PQRST codes.

Now define a Diagnostic Engine Object, much like the List-Based (LB)Engine described in U.S. Pat. No. 5,935,060, which is smart enough topit these objects against each other, in a race to be the first todiagnose itself. The engine is built to be smart enough to switch amongthe disease and symptom objects, so that nobody monopolizes thediagnosis. It is smart enough to know when to stop, to know when it isused for testing and when an emergency patient is online.

The following objects are described as examples of the kinds of objectsto be utilized by the system.

A. Disease Object

A Disease Object (DO) is a software object that represents an abnormalhealth state (illness, disease, disorder, cause) which we collectivelycall a “disease.” It is used in the method to establish the likelihoodthat the specified disease exists in the current patient.

The object's data are the basic properties of the disease and itsruntime state flags, such as:

the name of the disease,

its identification code,

its prevalence in the patient population,

a list of the component symptoms,

a list of symptom values and their diagnostic weights,

a list of alternative symptom values and their weights,

a list of synergies and their weights,

threshold values to be used,

a list of symptom values established in the patient,

the diagnostic score for the current patient.

The object's actions are the numerous functions and procedures needed bythe system to manipulate a disease, such as processes to:

pre-test the disease elements,

print the disease elements for review/editing by the author,

reset the disease for a new patient,

load disease data from the patient medical record (PMR),

diagnose the disease in the current patient,

report the diagnostic score,

write disease data to the PMR.

One of the Disease Object's built-in procedures is called “Diagnose”—afunction that diagnoses the current patient for the disease in question.This function invokes the Valuate function of one or more symptomobjects, which in turn invoke valuator objects to establish the value ofsymptoms by looking in the PMR, by calculating a formula, or by invokinga question object to ask the patient a question. Perhaps a better way topresent the diagnostic object is as a medically-trained software robot,an idiot savant that knows everything there is to know about only onedisease, and knows how to sniff it out in a patient.

The DO is used to capture all that is known about a given disease froman author and other sources, and to diagnose the disease in a patient atrun time. The DO can also be used to represent several related diseasesthat share common symptoms but diverge at some level of detail. Forexample, Malaria Falciparum, Malaria Ovale, Malaria Vivax, and MalariaMalariae might be combined into a DO Malaria and used to establish orrule out the basic symptoms of malaria before getting into details.

The DO can be used to subdivide a complicated disease into smallerdiseases. For example, is might be useful to divide Malaria into (1)Malaria in non-immunocompetant patients and (2) Malaria inimmunocompetant patients, to capture the different detailedmanifestations of the disease in these patient types.

B. Symptom Object

A Symptom Object (SO) is a software object that represents a patienthealth item (sign, symptom, complaint, presentation, manifestation,finding, laboratory test result (home or remote), interpretations of animaging study) which is collectively called a “symptom.” It is used inthe system to describe patient health in terms that the LB system canuse for diagnosis.

The object's data are the basic properties of the symptom and itsruntime state flags, such as:

the name of the symptom,

the type of symptom values (numeric, words, graphic),

the valid symptom values (NONE, LOW, MEDIUM, HIGH),

the name of the valuator object used to elicit the values,

the actual (runtime) values, over time, in the current patient.

The object's actions are the numerous functions and procedures needed bythe system to manipulate a symptom, such as processes to:

pre-test the symptom elements,

print the symptom elements for review/editing by the author,

reset the symptom for a new patient,

read/write past symptom data from/to the PMR,

valuate, i.e., establish the symptom value in the current patient,

report the symptom value,

report time-based synergy values (onset/offset, slope, trend, curve,area).

The SO may be thought of as a sort of software robot that knowseverything about one specific symptom, how to establish at a specifiedtime in the patient, and how to report it as specific values.

One view of the SO is as the fundamental unit of medical diagnosis, thequantum that is used to interface theoretical knowledge about disease toactual manifestation of disease in the patient.

Another view is that the SO plays the role of the variable in the“script language” of the method. By weighting them, the authorestablishes values of variables to look for, and by summing the valueweights, the system finds the diseases of interest to the author.

The basic use of the SO is to encapsulate all that is known about agiven symptom, at any point in the patient's lifetime. Symptom valuesare the units that are weighted in assessing the presence of disease inthe patient. The SO can also be used as a syndrome, to collect severalsymptoms that have medical significance as a group.

A symptom object describes the data and processing elements of any dataitem that can contribute to diagnosing a patient's disease. Inreal-world medical terms, a symptom object is known (depending on itscontext) as a symptom, sign, complaint, observation, test result,manifestation, report, presentation, and so on. In programming terms, asymptom object is a variable that can take on a specified range ofvalues in a patient.

Having said that, it is very important to understand that a SymptomObject is not limited to the classic signs and symptoms of medicine.While classic symptoms (e.g., pain, fever, headache) obviously play asignificant role in scripts, the Symptom Object is also used tomanipulate other data that somehow contribute to diagnosis, such as thepatient's habits, culture, environment, and even education. In addition,symptom objects can be used to build artificial internal data structuresas necessary. Thus, a symptom object can be used to define specialgroups of symptoms (syndromes, if you will), or to control the exactsequence in which the system elicits certain symptoms from the patient,or simply as convenient software containers that perform somecomputations or a table lookup to obtain a value. The Symptom Object isthe “work horse” of scripting, and this is reflected by the fact thatmany symptom objects are collected into a central system database thatcan be shared by all script authors, via the Internet.

A symptom object consists of the software elements needed to calculate a“value” that is used for diagnosing a given disease. Values aretypically obtained by asking the patient one or more questions, but theycan be obtained in other ways as well:

by accessing the patient's medical record,

by accessing the patient's responses to previous questions in thissession,

by logical reasoning, using specified implication formulas,

by mathematical computation, using specified formulas.

If a symptom value has been established by other means (such as from themedical record or by implication), the question will not be asked. Forexample, once the patient's birth date is known, the patient's age willnot have to be asked again.

In alphabetical order, exemplary aspects of a Symptom Object are asfollows:

Answerability probability that the patient knows the symptom Class whatkind of symptom this is (history, sign, custom, logic.) Documentationdescription and development history of the symptom object ICD ICD-9CMcode for the symptom Keywords search words to find symptom object in theindex Label name of the symptom object (not the symptom) Location wherethe symptom can be obtained Name formal medical name of the symptomOnset_Offset special onset/offset attributes Persistence how long avalue is good for, once obtained SNOMED classification code for indexingthe entire medical vocabulary, including signs, symptoms, diagnoses, andprocedures Synonyms alternate names of the symptom Trending specialtrending information such as the changes in severity of a symptom withtime or the evolution of symptoms in a disease process Valuator label ofthe object that actually obtains the symptom values Value current valueof symptom Value_Date date of last Value Value_Time time of last ValueValue_Type operational type of the value (integer, real, text, discrete)

C. Valuator Object

A Valuator Object (VO) is a software object that represents the actionsrequired to establish the value of a symptom in a patient at a specifiedtime.

The VO data are the basic properties of the symptom and its runtimestate flags, such as:

the type of valuation used (question, formula, graph, table),

the type of value reported (numeric, words, graphic),

the valid symptom values (NONE, LOW, MEDIUM, HIGH),

if applicable, the question object to be used,

if applicable, the mathematical or logical formula used,

if applicable, the graph, or table, or database to be used.

The VO actions are the functions and procedures needed by the system tomanipulate a value, such as processes to:

pre-test the valuator,

print the valuator formulas for review/editing by the author,

establish the value in the current patient,

report the value.

The basic use of the VO is as an interface between the symptom and thepatient level of abstraction. The VO can be used to present dummypatients to the LB system for testing. The VO can be used to switchamong lookup tables, based on global system control setting. The VOmakes an object out of an action, a common use of objects, so that wecan globally describe and control the actions that take place at somelower level.

D. Question Object

A Question Object (QO) is a software object that describes the softwareelements required to establish a mini-dialog of questions and responseswith the patient, in order to obtain a symptom value. It is the task ofthe QO to select the appropriate question set, to invoke the appropriatenode objects that actually question the patient, and to report back thepatient's response. A QO is a type of valuator object that specializesin interaction with a patient.

The Question Object is the point in defining a script where the authoractually writes a script, albeit typically a very short one, that isfocused on asking about one specific symptom. This mini-script is brokendown into separate node objects, each of which presents a Preamble, aQuestion, and a set of labeled Buttons to the patient, and obtains aresponse from the patient. The QO data are those elements required toask a question and obtain a response from a patient, such as the list ofnode objects to be used.

The object's actions are the functions and procedures needed by thesystem to manipulate a question, such as processes to:

pre-test the question and node elements,

print the question elements for review/editing by the author,

ask the question and report the response,

specify the actual natural language text to be used,

establish the user interface required for the current platform,

invoke a node object to actually ask the question and report theresponse.

The QO is another interface object, used to separate the questioner fromthe language used to question the patient. The basic use of the QO is tohandle the details required to present a (possibly complex) question toan online patient. The QO can be used to change the educational level ofthe question text (Question Roller). The QO can be used to changenatural language used to speak to the patient.

E. Node Object

A Node Object (NO) is a software object that describes the softwareelements required to ask a single, well-defined question of the patientand to return the response selected by the patient. It is the task ofthe NO to present the required data to the GUI in a form that willappear user-friendly manner on the user display, to wait an appropriateamount of time for a user response, to possibly re-prompt the user, andto ultimately return the user's response.

Node objects operate at the lowest level of the script hierarchy; theyinterface to the operating system's user interface. Computation dependson the platform used. For a Windows operating environment, the nodewould display an appropriate window containing sub-windows for thePreamble and Question Text. Next it would display the requisite numberof buttons and display the form to the user. When a button is pressed bythe user, the node object returns the index number of the response.

The NO data are those elements required to ask one detailed question,obtain a response, and return the index number of the response. The NO'sactions are the functions and procedures needed by the system to displaya question to the user, such as processes to:

pre-test the node elements,

print the node elements for review/editing by the author,

display the question and report the response.

The NO is another interface, between the script objects and the patient.The basic use of the NO is to handle the low-level details required to“talk” to a patient. The NO can be used to port an application toanother hardware platform or operating system. The NO can be used to“fake” a patient by taking inputs from a test file and writing outputsto a test result file. The NO can be used to log all questions to, andresponses from, the patient, time-stamped to the nearest hundredths of asecond if necessary. One of the reasons for defining Node Objects aswell as Questions Objects is that the entire system can be translatedinto other languages by translating all of the Node Objects.

IV. List-Based Engine Concepts

In one embodiment of the invention, the List-Based Engine (LBE) is oneembodiment of the diagnostic processing method. It is a program that,essentially, takes a set of diseases (more precisely a collection ofdisease descriptions, symptom definitions, and question specifications)and processes them against one specific patient.

The patient is typically a human that can conduct an interactive dialogwith the system and can respond to questions posed by the system.Alternatively, the patient may be represented by a medical record inwhich some or all symptoms already have values, so that the systemsimply sifts the values and scores the diseases accordingly. For testpurposes, the patient may even be represented by a computer program thatis “playing patient” in order to test the system's ability to respond toabnormal situations such as unexpected key presses, extensive responsedelays, contradictory answers, requests for repeating a question, andabnormal termination of a session.

For a specific run or session, the system begins its work by gathering aset of candidate diseases that it is supposed to diagnose. This initialcandidate list is most likely assembled by a module that has analyzedthe patient's Chief Complaint and selected appropriate diseases from adatabase that is indexed by chief complaint. In the absence of a chiefcomplaint, the system can just start with all the diseases it finds in agiven project file, where an author wants to test a newly created oredited script.

Once it has a list of candidate diseases, the system's job is to processthese diseases, typically by asking questions and accumulatingdiagnostic scores for each disease until some specified system goal isreached. This system goal is expressed by the system “Mission” setting,which can specify various goals such as “run all diseases” or “run untilthe first disease is ruled in” or “run until 10 minutes have passed”,and so on. The default system mission is to “run all diseases until allsymptoms have been evaluated”.

Diagnostic Loop

In one embodiment, the system uses a “diagnostic loop” to process thecurrent disease list. Portions of the diagnostic loop have beendescribed in Applicant's U.S. Pat. No. 5,935,060, which is herebyincorporated by reference. The diagnostic loop consists of a series ofiterations in which the system considers its mission in the light of thelatest status of all candidate diseases. Depending on the mission, thesystem can perform all kinds of special calculations and evaluationsduring this loop. The loop actually consists of several nested loopsthat may involve recursion to evaluate subordinate symptoms.

Current Disease

In one embodiment, during the diagnostic loop, the first aim of thesystem is to determine which disease it should evaluate next, based onits mission. The mission might be to “evaluate the disease with thehighest score”, or “evaluate the disease with the highest diagnosticmomentum”, or “evaluate any random disease”. The default mission is toevaluate the next disease as originally given in the candidate list.

Current Symptom

In one embodiment, once it has a “current disease”, the next system aimis to determine which symptom of the current disease it should evaluatenext. The mission might be to “evaluate the symptom that can add thehighest weight to the score of the current disease”. A more complexmission might be to “evaluate the symptom that will advance the score ofthe most diseases”. The default mission is to evaluate the next symptomin the symptom list of the current disease.

Current Evaluation

In one embodiment, evaluating a symptom consists of establishing thevalue of the symptom for a specified date and time in the patient'slife. How this is done depends on the type of symptom and on the type ofthe valuator object defined for the symptom. A symptom may already havea valid current value in the patient's medical record. For example, thepatient's gender may already be in the medical record, in which case thesystem obtains it and continues. The patient may already have suppliedthe symptom value during the current session in the context of aquestion for some other disease. Again, the system obtains the symptomvalue from the current session record. (This feature avoids asking thepatient the same question in the process of evaluating differentdiseases.) Many symptoms are evaluated by running a Question object,i.e., asking the patient one or more questions. Symptoms may use a Logicobject to evaluate a value; this means that the system parses and runs alogic formula, such as “if patient has symptom value A and has symptomvalue B, then the value of this symptom is C”. To evaluate this symptom,the system would (recursively) evaluate symptoms A and B and thenestablish C, if appropriate.

Scoring

In one embodiment, after the system establishes a new symptom value, itupdates the scores of all candidate diseases. Depending on thedescription of each disease, scoring can consist of simply adding theweight corresponding to the new current symptom value, or it can involveadding special synergy weights based on the values of other symptoms, oron the timing of symptoms. Scoring can also include establishingprobabilities of diagnosis, which typically depend on the existence ofseveral symptom values, sometimes in a defined time order. Finally,scoring includes evaluating the scores of diseases against variousthresholds. Depending on the system goals, a disease may be placed intoa special category based on its score. For example, a disease may bedeemed “ruled in” when its score reaches or exceeds a specifiedthreshold, or it may be placed on a special diagnostic momentum track ifits score is increasing more rapidly than other disease scores. Thedefault system goal is to add the symptom weights to all applicabledisease scores.

Continuation

In one embodiment, after the system has updated the scores of alldiseases, it determines how to continue by considering the set of newscores. Again, the system's goals can specify different actions for thesystem, such as “stop when any score exceeds 1000” or “stop when thediagnosis has been ruled in” or “stop when the system has the five mostlikely diagnoses” or “stop when ten minutes have elapsed”. The defaultgoal is to run until all symptoms of all diseases have been evaluated.

A. Dynamic Rules and Goals

In one embodiment, the system is designed so that the rules, limits, andgoals that govern the diagnosis can be changed at run time. The systemmay use tables of rules and goals and limits, of which the applicableset is selected as needed.

For example, at the top of the diagnostic loop when the system selectsthe next disease to be considered, it can use any one of a number ofrules such as “Select the disease that:

is the most life-threatening disease remaining to be diagnosed,

shares the most symptoms with other diseases,

has the highest current diagnostic score,

has the highest current change in diagnostic score,

has the fewest unresolved symptoms,

is next in some order specified by the author.”

Similarly, when the system selects the next symptom with a disease, itcan choose it based on various dynamic modes or control variables.

The patient him/herself can set certain boundary conditions on theconsultation. Several examples include:

a patient who has only 20 minutes to talk

a patient who wants only to exclude a certain disease (“e.g., my friendhad a headache like mine, and he was diagnosed with brain tumor”)

B. Diagnostic Momentum

In one embodiment, the “diagnostic momentum” is the rate of change ofthe diagnostic score of a candidate disease. It provides a measure ofhow fast a given disease is accumulating diagnostic weights, compared toother competing candidate diseases. The system tracks the score and themomentum for all candidate diseases and can use this information tochange the diagnostic mode. Note that the use of various synergy weightswill add extra weight to diseases with many matching symptoms, so thatpositive feedback is established that tends to favor diseases with manymatching symptoms and thus to converge rapidly on one disease (see e.g.,Sequencing Synergy and Summation Synergy).

As the LB method diagnoses, it tracks for each disease the latestdiagnostic score, the last change in the score, and the name of thedisease with the largest momentum during the current iteration of thediagnostic loop.

Since the name of the disease with the highest momentum is available atall times to the LB engine, it can be used to guide the diagnosticprocess itself and to check whether any goals or limits or decisionpoints have been reached. It provides the LB method with feedback thatlets it feel its way along a diagnostic path in a manner that isstrongly driven by the patient's responses. For example, the faster adisease is approaching the diagnostic threshold, the more intensely theLB method can focus on disease.

This feature simulates the manner in which a human doctor sifts hisknowledge of disease based on what s/he is learning about the patient'scondition. As the symptom pattern begins to match the pattern of aspecific disease, the doctor will ask questions designed to confirm (orreject) this disease.

The advantage of the momentum feature is that it (1) quicklyde-emphasizes many less relevant diseases, (2) minimizes questions askedof the patient, and (3) cannot be done as rapidly and as precisely bythe human doctor as by the computer.

C. Horizontal Axis of Inquiry (HAI)

In one embodiment, the system conducts its diagnostic inquiries alongvarious “axis”, i.e., lines of investigation or focus directions. Wecall two of these strategies the Horizontal Axis of Inquiry (HAI) andthe Vertical Axis of Inquiry (VAI). This section focuses on the HAI.Note: The “inquiry axis” terminology relates to the manner in which thesystem selects the next focus symptom. The terminology derives from theDisease/Symptom Matrix (DSM) metaphor, in which a table is formed byarranging the candidate diseases as side-by-side columns (hence“vertical”) and the component symptoms as rows (hence “horizontal”). Seethe DSM figure. In database terminology, fields are arranged along thevertical, and records arranged along the horizontal. The Horizontal Axisof Inquiry (HAI) strategy is a diagnostic mode that focuses on quicklyeliminating inapplicable diseases from a large list of candidates. HAIis typically used early in a diagnostic session, when the system hasnumerous candidate diseases and selects focus symptoms based more on howmany diseases contain the symptom than on how effective the symptom willbe in identifying one disease.

Other diagnostic methods have one and only one method. The presentinvention, by contrast, allow many different modes of inquiry, which arethemselves dependent on the progress of the diagnosis. In both the HAIand VAI strategies, the LB engine updates the scores of all candidatedisease scores with the responses obtained from the patient. Thus, thedifferences in these strategies relate primarily to how the systemselects the next focus symptom, not how the candidate disease scores areupdated.

In the HAI mode, the Alternative Symptom (AS) feature will typically beactivated, so that fewer and more general questions tend to be asked. Inthe VAI mode, the AS feature may or may not be activated, depending onthe need for more detailed responses from the patient.

The choice between HAI and VAI strategies is very important because itpermits general “sifting” of many candidate diseases as well as focusingon diagnosis of one specific disease, at a detail level where thepatient can—indirectly—interact with the script author, a worldspecialist on that disease. Other medical diagnostic systems typicallyinteract at one and only one level with the patient.

The decision which one of these (or some other) strategies or modes toselect can be programmed to depend on any number of variables. Forexample:

it may be specified by the process that calls the system;

it may be modified based on the goal selection routine that runs earlyin the consultation;

it may be switched based on the Diagnostic Score or Momentum reached byone or more diseases;

it may be switched by various computations related to the ChiefComplaint or the First Significant Symptom;

it may be switched based on a new response by the patient, which negatesor significantly modifies a previous response.

In the HAI strategy, the system searches the list of candidate diseasesand their symptom lists to find symptoms shared by many diseases. Itselects such a shared symptom and evaluates it, typically by asking aquestion, or by evaluating a formula or a logic structure. Then itupdates every disease with the new value of the symptom, and adds theappropriate weights to each disease score.

In the HAI strategy, the system can sort the candidate diseases by thenumber of shared symptoms to prepare for an efficient subsequentelimination process. For example, by establishing the gender of thepatient, the system can eliminate all gender-specific disease. The HAIstrategy permits the system to partition the candidate diseases intouseful classes so that it can focus on promising classes first. Forexample, it might partition diseases into the following categories:urgent, serious, common, or it might partition diseases into promising(high probability that the diagnosis is among them), intermediate andlow probability.

D. Vertical Axis of Inquiry (VAI)

The Vertical Axis of Inquiry (VAI) strategy is used to examine onecandidate disease in detail, so that the system selects the next focussymptom repeatedly from the same disease. This strategy is intended togive one specific disease that has scored significantly the chance ofestablishing itself as a diagnosis. The VAI strategy is equivalent toletting the script author (1) ask several successive questions aboutthis disease and (2) ask his or her preferred questions, in cases wherethe patient has previously answered Alternative Symptoms.

In the VAI strategy, the LB engine evaluates the various symptoms of onedisease. Symptoms can be selected in various orders, depending on theengine mode. In one embodiment, the script author may prescribe asequence in which symptoms are evaluated, but this can be overridden byfirst asking for symptoms that carry the most weight, or for symptomsthat are the easiest or fastest for the patient to answer. The systemselects such a shared symptom and evaluates it, typically by asking aquestion, or by evaluating a formula or a logic structure. Then itupdates every disease with the new value of the symptom, and adds theappropriate weights to each disease score.

In the VAI strategy, a patient who has earlier answered questions usingAlternative Symptoms (see there) can now have the opportunity to beasked the symptoms which the author defined. This has the effect of“fine-tuning” the answers to the specific disease at the point when thedisease is becoming a contender. In this way, a patient can be promisedthat no matter what disease they have (if the system covers thatdisease) they can be guaranteed to interact with a dialogue created by aworld-class specialist in that disease.

The VAI strategy can be set to use only the author's own symptoms,instead of accepting the (normally alternative) symptoms of otherauthors. This means that the system can (perhaps at the patient'srequest) re-ask all symptoms using only the authors' own questions.This, in turn, means that the patient's entire consultation on a givendisease can ultimately be conducted using the world expert on thedisease. This gives the LB method the ability to shift from the broad,generalizing viewpoint (where it accepts all alternative symptoms) tothe narrow, specific viewpoint, where the world expert's questionsphrasing may help to distinguish among close diseases.

The HAI and VAI strategies are part of the central processes for symptomselection of the system, specifically the LB Diagnostic Loop. Thedecision which one of these (or some other) strategies or modes toselect can be programmed to depend on any number of variables. Forexample, it may be specified by the process that calls the LB engine; itmay be switched based on the Diagnostic Score or Momentum reached by oneor more diseases; it may be switched by various computations related tothe Chief Complaint or the First Significant Symptom; it may be switchedbased on a new response by the patient, which negates or significantlymodifies a previous response.

The VAI and HAI strategies permit the system to vary its diagnosticfocus from the general to the specific. In the early stages, the engineknows little about the patient and must ask the best general questionsthat quickly eliminate large numbers of candidate diseases. But afterapplying the HAI strategy for a while, if the diagnostic momentum ofsome disease D reaches a specified level, the engine can then switch tothe VAI strategy to focus diagnosis on disease D, to the momentaryexclusion of all other diseases. It is important to note that all of thedisease object (experts) “monitor” all of the questions and answersgenerated by other disease objects. After applying VAI for a while,disease D may emerge as the “front runner”, or it may fade, beingoutstripped by one or more other disease scores. One of these may thenbecome the driver of another VAI round, or the diagnostic strategy mayrevert to HAI if no disease has a clear lead.

There is a powerful holistic effect when various LB features such asDisease Momentum, Dynamic Goals, HAI, VAI, Alternative Symptoms, andSynergy Weighting are combined. Consider how the LB engine sifts thecandidate diseases and converges on the appropriate ones: As one diseasegains score and momentum in the HAI strategy, this triggers a shift toVAI strategy. If the system is “on the right track”, the VAI strategywill rapidly confirm that several key symptoms of that disease arepresent in the patient. Through the various Synergy weights, thisconfirmation will increase the score and the momentum, and reinforce thecycle to converge on the focus disease as a diagnosis. In oneembodiment, the symptom weights may be increased when the system isoperating using the VAI strategy. This feature allows the system toaccommodate Bayesian probabilities in the evaluation process. On theother hand, if the system is “on a cold trail”, the VAI strategy willfail to confirm additional symptoms, the disease score will lag behindthose of other diseases (which are being updated in parallel) and thesystem will soon abandon this fruitless pursuit and either return to theHAI strategy, or select another disease for a VAI inquiry.

E. Alternative Symptoms

In one embodiment, the Alternative Symptom feature of the LB method letsa disease author specify a set of symptoms that are alternative to aspecified symptom for the purpose of diagnosis. The invention lets anauthor specify alternative symptoms that can take the place of theauthor's preferred or specified symptom, perhaps with a differentweight. The feature is designed to solve the problem that differentauthors may prefer different ways of asking a patient about the samesymptom, yet we do not want the patient to have to answer questionsabout the same symptom over and over.

The LB engine is programmed with alternate modes that either do or donot permit symptom alternatives. When permitted, the system accepts thevalue of any alternative symptom as the value of a symptom; if notpermitted, the system requires the asking of the author's specifiedsymptom, even if this requires asking the patient certain questions asecond time. One goal of the LB diagnostic method is that, no matterwhich disease the patient has, he or she will be asked questions by aworld-class specialist in that disease. This feature mimics how humandoctors interview a patient: In the early part, the doctor asks broadquestions that determine the general overall nature of the patient'sdisorder. Once one disease emerges as a likely diagnosis, the doctorasks more specific questions that confirm or reject the hypothesis tosome degree. Finally, when the most likely diagnosis seems almostobvious, the doctor asks even more detailed questions in order torepeat, emphasize, seek more details, add confirming symptoms, and soon. These final question may well repeat questions asked earlier,perhaps to give the patient a final chance to confirm earlier responses.

The Alternative Symptoms feature gives the patient the option to go backand answer questions exactly as worded by the original disease scriptauthor, or to simply accept the questioning by the alternative symptoms.This is analogous to a computer user who installs an application who caneither insist on a “custom installation” or accept the “typicalinstallation”.

At scripting time, when the author of a disease script first lists thecomponent symptoms of the disease, the author can either specify brandnew symptoms, which the author writes from scratch, or specify existingsymptoms, which the author retrieves from a database of stored orarchived symptoms that is shared by all authors. This initial symptomset becomes the author's preferred or specified symptoms, which theauthor prefers to be asked of the patient. Next, the author reviews thesymptom database to see which symptoms are so “close” to his/herspecified symptoms that they can serve as alternatives. The author liststhese alternative symptoms and assigns some diagnostic weight to them.One author's specified symptom is another author's alternative symptom.Thus all symptoms are specified symptoms to some author, who isresponsible for maintaining their currency.

Each of the authors may be linked by a data communications network suchas the Internet. When a new symptom object is created by Author A, acopy of the new symptom object is instantly “sent” to the authors of thediseases in which his symptom is also used, e.g., Author B. This thenwould be an alternative symptom for Author B. Author B then assigns aweight for the disease he is authoring when this new alternative symptomis used in a question.

At run time, the system can either allow or disallow the use ofalternative symptoms. If the system is in the alternative symptom mode,and the system is seeking the value of specified symptom S1, it mayaccept the value of any alternative symptom in its place. The effect isthat, if the patient has already been asked about any alternativesymptom S2, S3, or S4, the system will not ask the patient again, butwill accept the alternative symptom and its weight. If the system is notin the alternative symptom mode, when the system seeks the value ofspecified symptom S1, it will proceed to ask the questions associatedwith symptom S1.

The Alternative Symptom feature eliminates redundant questioning of thepatient and permits the author to group symptoms together that have thesame impact on his disease. The Alternative Symptom feature lets theauthor control how she or he wants to focus on symptom details, i.e., onthe quantization of symptoms. For high-level diagnosis, a high level ofquantization may be sufficient; at a later time, the author may needmore precise details, such as to distinguish between close variants of adisease.

In one embodiment, the system symptom database may contain severalthousand symptom script elements, written independently by severalhundreds of authors. Many of these symptoms may be the same, or beacceptably similar variations of each other. Without AlternativeSymptoms, the system would load all candidate diseases. In the course ofrunning them, the engine might encounter some of these similar symptomsseveral times. The effect would be to ask the patient the same questionin many different ways, which would be inefficient and would notengender confidence. But with the Alternative Symptom feature, after thesystem evaluates any one of the alternative symptoms, the other symptomsin the set will not be asked.

A benefit of the object-based system having Symptom Objects and usingthe Alternative Symptom feature is that Symptom Objects and theirunderlying objects, e.g., Valuator Objects, Question Objects and NodeObjects, can be “reused”. In one embodiment, the author of a new diseasescript can reuse previously written and debugged objects by a few steps,which may include, for example, renaming one or more of the objects andassigning alternative weights. This object reuse capability permitsfaster coding, testing and release of new disease scripts.

F. Disease Timeline

In one embodiment of the invention, the Disease Timeline may be a chartor graph that describes how each symptom of the disease manifests itselfover time in a typical patient. The timeline is a characteristic“pattern” of the disease that can be used as a reference for comparisonsof the patient's actual symptom time chart.

This aspect of the invention relates to pure medical knowledge about adisease; it is independent of any one patient. This aspect is“theoretical”, in contrast to a Symptom Time Chart, which relates to the“actual” symptom values as experienced by a patient over time.

The timeline is for a generic occurrence of the disease, to serve as abase reference. It can be scaled to fit a given patient.

At design time, the author of a disease object describes the typicalcourse of the disease in terms of how and when its symptoms typicallyarise (onset), vary, and subside (offset) over time. This timelinestarts with the First Significant Symptom (FSS) of the disease, and alltimings are based on the start of the FSS. Note that the FSS may bedifferent than the patient's chief complaint.

One embodiment utilizes a Gantt chart that records the times of theappearance, disappearance, overlap, and other aspects of the componentsymptoms. Initially, the author might only choose three time points foreach symptom; later, more and more points can be added. A typical goalis an hour-by-hour description of the disease.

At run time, the system matches the patient to the script. Appendicitismay be used as an example disease to walk through a simple diagnosis.Assume that the author has chosen to describe the disease as follows:The first symptom is often (though not always) anorexia, so this symptomis the origin for the timeline. Anorexia, then, occurs at 0 hour. Athour 1, one typically expects nausea. At hour 3, one expects epigastricpain to become noticeable to the patient. By hour 8, one can expect thepain to be migrating to the right lower quadrant of the abdomen, and soon.

At run time, when a patient enters the system, the system preferablyasks when the chief complaint started. In one embodiment, the systemthen selects the script that is nearest in time. So, here is a patientwith appendicitis calling the diagnostic system; she or he may, ofcourse, be at any stage along the disease timeline. Usually anappendicitis patient waits until she or he has abdominal pain beforeseeing a doctor. So, let's say our patient presents abdominal pain of agiven severity as the chief complaint.

The system (in HAI mode) then searches all candidate scripts forabdominal pain of our patient's severity. It finds the appendicitisscript, which indicates where a patient with that severity should beplaced along the time line. The disease object can now compute the timeoffset required to match the patient, and can “place” or “match” thepatient to that point in time in the appendicitis script.

Sooner or later, the LB system will let the appendicitis script askanother symptom. The script will ask the patient about earlier nausea oranorexia, and—if the patient confirms—will add weight to the score ofappendicitis. At some point, the rising score will trigger the system toswitch to VAI mode, and to ask about several more symptoms from theappendicitis script. This may rapidly pile on more weight, and theappendicitis diagnosis would then exceed threshold and would be ruledin. If not, the system will know what symptoms should appear next, andlet the patient know.

The chart, graph or timeline described above may also be referred to asa predetermined template of symptom characteristics. One or more of theestablished symptoms may have symptom characteristics that arise (onset)or subside (offset) over time so as to match the predetermined template.If so, additional weight is added to the score for the particulardisease. Furthermore, if the onset or offset characteristics match thepredetermined template and a set of the established symptoms occur in aspecified sequence over time, still more additional weight is added tothe score for the particular disease. Thus, it can be seen that whencertain symptom conditions are met, the score of a particular diseasemay rapidly reach the disease threshold and be ruled-in or diagnosed.

A disease needs time to “declare itself.” On one hand, the longer onewaits in a disease process, the more certain they can be of thediagnosis; on the other hand, one wants to make the diagnosis as soon aspossible to begin appropriate treatment.

The author actually has two “clocks”. One clock is related to theappearance of the Chief Complaint, the other clock is related to theappearance of the First Significant Symptom. The HAI mode uses the CCclock, while the VAI mode uses the FSS clock, which is more accurate,but cannot be used until one has a tentative diagnosis.

See FIG. 28 for an exemplary screen shot of a user interface forspecifying the order of a particular set of symptoms so as to establishthe First Significant Symptom. The user may for example, slide symptombars along the time axis to indicate their particular symptom history.The user would then click on the “submit” button which causes the newsymptom occurrence times to be captured and then evaluated by thesystem.

The author can also use the symptom timeline as a characteristic patternof symptom magnitudes. This is useful in describing and differentiatingdiseases based on their symptom patterns.

G. Spectrum of Terms/PQRST Code

In one embodiment of the invention, the PQRST Code is a comprehensivemethod for capturing and encoding a patient's verbal description of asymptom. It is particularly suitable for highly subjective symptoms thatare hard to quantify, such as the patient's overall health, thecharacterization of a particular pain, or the expression of a mentalstate or emotion. The key invention here relates to the “Vocabulary ofDiagnosis.” This refers to the ability of the LB method to let an expertauthor use the exact vocabulary she or he has developed over years ofexperience in questioning the patient. In the real world, certain wordsused by patients to describe pain are classic indicators of specificdisease. In the LB world, this is implemented by letting the patientselect from a pick list of words that are then associated with apredetermined diagnostic weight. The PQRST Code may be used to trackchanges in other health data such as lesions, masses, discharges, bodyfunctions, mental states, emotions, habits, addictions, and so on.

Pain is a subjective experience of the patient. It is diagnosticallyhighly useful, yet practically very hard to describe in sufficientlyuseful detail. The PQRST Code is a comprehensive method for encoding apatient's description of pain, and for using the pain code for diagnosisin the LB method, and for other purposes such as Advice, Prescription,Treatment, Pain Management, and Disease Management. In the LB diagnosticmethod, the PQRST Code may be used to encode subjective symptomdescriptions, to capture changes in symptom descriptions, and to analyzechanges over time. Not only may the PQRST Code itself be composed ofhundreds of elements, but the possible uses of the code in medicalautomation are manifold. The PQRST Code is directed to manipulatingmedical knowledge in an automated way. The basic idea is using wordspectra and pick lists to capture a patient's subjective description ofsome health experience. The PQRST Code may then be used to detectsymptom changes, slopes, trends, areas, and so on, where change is thekey. The PQRST Code feature includes picking words from a word spectrumat two points in time, and then analyzing the significance of thechange, and using this to give extra weight to one diagnosis. Thisfeature places words in the spectra that do show how a particular aspectof pain is likely to change over time, and then does the secondevaluation and gives extra weight to one diagnosis because it manifestedthe expected change.

The PQRST Code feature includes methods for:

describing some 20 aspects of pain,

obtaining these aspects from the patient,

encoding and decoding these aspects as a single PQRST code,

using the PQRST code in diagnostic and other contexts.

At the global level, for all authors and all script, we define some 20aspects of pain, such as Quality, Severity, Location, Size, Symmetry,Timing, Localizability, and Migration aspects. For each aspect, wefurther define a word spectrum that consists of a set of words commonlyused by patients to describe that aspect of pain. For example, theQuality of pain might be described in terms of “pinprick, knifing,tearing, fullness, tightness, pressure.” The Severity of pain would berated by the patient on a scale of 0 to 10. Word spectra are, of course,different for different aspects of a symptom. Non-pain symptoms mightrank some aspect like “Age” along a numeric scale such as 0-7, 8-22,23-65, and 66 and over. Another spectrum might use words such as NONE,LOW, MEDIUM, HIGH to characterize an aspect. Or, a word spectrum mightconsist of a vocabulary of descriptor words such as PULSING, POUNDING,HAMMERING, TAPPING. The script author defines diagnostic weights foreach word of a spectrum. At run time, a given spectrum is presented as apick list from which the patient can choose. The patient picks one wordfrom the list, and the system adds the associated diagnostic weight tothe score.

The PQRST Code feature permits authors to apply the vocabulary ofdiagnosis that they have developed over years of experience. A scriptcan use several word spectrum symptoms to build up a PQRST Code thatsummarizes the state of health of a patient at some time “t”. This codecan be stored in the patient medical record (PMR) for later use. This isanother example where a symptom object specialized for word spectra maybe defined. The script can collect a PQRST Code for different times T1,T2, T3. The script can then analyze the changes in code over time, andassign weights for significant symptom changes over time. The script canuse the PQRST code to compute synergies based on slope, trend, area,volume, and other properties.

Frequently during the same consultation, the severity of the patient'ssymptoms is trended. In addition, many PQRST array spectra can be askedat the beginning and at the end of the same consultation. The reenterfunction (second consultation for the same disease process) and there-reenter function (third consultation for the same problem) are usedin concert with the PQRST array to evaluate the evolution of the diseaseprocess to make the diagnosis.

Each author is able to use or re-use the word spectra that are alreadycreated. Each spectrum is typically 7 to 11 adjectives that arecarefully selected. For example, if a patient's epigastric pain(location) which cannot be localized (localizability) moves (migration)to the right lower quadrant (location) and now is easy to localize(localizability), the patient has appendicitis.

The diagnostic system can collect and publish medical statistics on the“vocabulary” that is used in diagnosis. The diagnostic system can usethe vocabulary as a “digitized medicine” to fine-tune scripts and theiractions.

The following is an example of PQRST Code that tracks the nature of adischarge, instead of a pain. The Mallory-Weiss syndrome consists of apartial thickness tear in the very inferior aspect of the esophagus. Itis caused by severe vomiting. Hence a patient who is vomiting food attime “t” and vomiting food with blood in it one hour later hasMallory-Weiss syndrome, compared with a patient with a gastric ulcer,who would have blood in the vomit from the beginning. Therefore, asymptom built around PQRST encoding of vomit contents will detect theaddition of blood and add the appropriate synergy weight to theMallory-Weiss disorder.

H. Synergy

In one embodiment of the invention, and in the context of automatedmedical diagnosis, “synergy” means adding extra diagnostic weight to adisease if a symptom occurs in the patient in a specified manner,intensity, anatomic location, frequency, sequence, combination withother symptoms, or similar pattern. The synergy concept provides a wayfor an automated diagnostic system to take into account the symptoms ofa patient viewed as a holistic pattern that can be used to incrementallyrefine the ranking of a disease for the purpose of diagnosis.

The word “synergy” may have the meaning of “combined effect.” It refersto accounting for the special additional impact on diagnosis of the factthat a symptom is occurring, changing, or interacting with othersymptoms in the patient in some well-defined manner with respect totime, anatomic space, quality, sequence, frequency, combination, mutualcausation, and so on. In short, the synergy concept implements insoftware the medical fact that the diagnostic significance of acombination of symptoms is much greater than the significance of each ofthe symptoms in isolation.

For example, applied to the LB diagnostic method, the synergy conceptsignificantly enhances the capabilities of the method, because theweighting mechanism of the LB method can be used to detect and accountfor the presence of synergy in the patient's reported symptoms. In fact,synergy allows the LB engine to dynamically adjust the very diagnosticprocess itself after every response from the patient.

The synergy invention approximates the cognitive process of a humanmedical expert by providing for non-linear weighting of symptoms, byincrementally adding small weights to account for fine differences inpatient health states, by fine-tuning the diagnosis, and by dynamicallyguiding the diagnostic process itself into productive channels.

Using synergy, the symptom object of the LB method becomes a smartprocess that does not just store symptom values, but that can performdynamic intelligent internal analysis of how the symptom behaved overtime in the patient, which generates useful diagnostic information inits own right.

In the context of certain embodiment of the LB diagnostic method, theword “synergy” has the normal dictionary meaning of “combined operationor action”. Again, it refers to measuring the special, additional impactof several symptoms or symptom changes being present at the same time orin some prescribed sequence. The synergy concept implements in softwarethe real-world medical fact that the diagnostic significance of asyndrome is much greater than the significance of its component symptomsin isolation.

As detailed later for every individual synergy type, the synergy conceptsignificantly enhances the LB method, so that special health conditionsand their changes in a patient can be:

detected by suitable questioning or computation, and

assigned diagnostic weights in advance, then

combined logically and mathematically, and

used to score candidate diseases, which are

used to rank candidate diseases, which are finally

used to select those diseases that the patient most likely has.

The system diagnostic methods include a novel and non-obvious way tocompute a medical diagnosis. By this method, an medical script authorcan describe in advance certain specific health conditions and effectsin a patient which tend to be less obvious and more difficult to detectby other methods. In certain embodiments of the present automatedmedical diagnosis system, synergy means special manifestations in theanatomic systems, over time, of patient-specific symptoms andpatient-driven verbal descriptions of symptoms. The synergy inventionmimics human cognition by providing for non-linear weighting ofdiagnoses by incrementally adding fine differences in health states, byfine-tuning the initial diagnoses, i.e., by allowing the medicaljudgments of the script authors to be implemented in an automatedmanner. Synergy allows the LB engine to watch and to dynamically adjustthe very diagnostic process itself after every response from thepatient.

Recall that the LB method defines a “symptom” as any patient data itemthat can affect diagnosis. Therefore, all of the mechanisms used by theLB method to select, evaluate, and record the impact of symptoms areavailable and used to handle synergies. At script writing time, authorsdefine synergies and assign weights to them like any other symptom. Ifthe author intends to weight the, say, symmetry of onset and offset, theauthor defines a symptom and a question that will elicit the informationdirectly from the patient or indirectly from other data such as thevalue of other symptoms. At run time, the LB engine, whether in theHorizontal Axis of Inquiry or in the Vertical Axis of Inquiry, selectssymptoms, evaluates them, and—if applicable—adds the associated weightto them.

One feature of the LB method that bears noting, is that it diagnosesdisease by assigning “weights” to the patient's symptoms and then usesthe weight accumulated by a set of candidate diseases to determine whichdisease(s) the patient most likely has. The basic weight assigned is forthe mere presence or absence of a symptom. Now, under the synergyconcept in certain embodiments, there are two additional ways to analyzemore detailed aspects of symptoms. First, for each individual symptom,the system can diagnose based on whether it is “first” for a givendisease, and on the manner in which the symptom starts, varies, andstops. Second, when several symptoms are present, the system candiagnose based on their presence as a combination, their sequence andextent of overlap in time, and their relationship to (and change in) theanatomic systems of the patient. In other words, these synergisticweights are “refinements” of the fundamental weighting. They specify indetail the considerations for which the LB method can add extradiagnostic weights to a disease. This idea is referred to as “synergyweighting”; it reflects the fact that more detailed knowledge about oneor more symptoms of a patient can be used to refine and focus thediagnosis.

The following table lists several type examples of synergy that can beimplemented. The examples are, of course, not exhaustive; they can beextended to any special pattern of one or more symptoms occurring in apatient.

This synergy adds weight to a Synergy Type disease if it detects that .. . Symptom Presence patient has symptom A (the basic weighting concept)Symptom Level symptom A has specific value (PAIN SEVERITY = 8 out of 10)Time-Based Synergy symptom A varies, cycles, pulsates, comes/ goes,repeats Onset/Offset Slope symptom A starts or stops at a specified rateOnset/Offset Trend the rate of symptom A is changing in a specified wayOnset/Offset Symmetry symptom A starts and stops in a similar mannerFirst Significant patient has the same FSS as defined for the Symptom(FSS) disease Simultaneous patient has a specified symptom set A, F, J,and R. Sequencing symptoms A and B occur in a specified sequence OverlapSynergy the length of time that symptoms A and B occur together IntegralSynergy the area under the curve of, for example, plotting the severityof a patient's pain over time

The following sections relate to describing and weighting specificsynergy types in certain embodiments of the invention.

1. Symptom Presence Synergy

Symptom presence synergy assigns basic diagnostic weight to a candidatedisease if a given symptom is present. At design time, the author canassign a weight to a symptom if it is present. For example, if thepatient has a smoking history of ten pack-years, the disease EMPHYSEMAmay get 50 points; if the patient recently went on a jungle expedition,the disease MALARIA may get 50 points. At run time, the systemdetermines if the symptom is present in the patient and assigns a weightto all diseases for which the symptom has been pre-defined with aweight.

Knowing the presence of symptoms, even without a value or timereference, can help to select candidate diseases for subsequentdiagnosis. Thus, a different set of candidate diseases can be selectedinitially for a patient complaining of COUGH than for a patientcomplaining of BACKPAIN.

2. Symptom Level Synergy

Symptom level synergy assigns diagnostic weight based on a level of asymptom present in a patient. At design time, the author defines severallevels for a symptom, and weights for significant levels, such as:

SEVERITY = 0 0 points; SEVERITY = 1 10 points; . . . SEVERITY = 9 250points; SEVERITY = 10 350 points.

At run time, the system determines: (1) if the symptom is present and(2) at what level and, if so, (3) adds the corresponding weight to thedisease score.

The author can define symptom magnitude with any appropriate resolution.This is obviously very useful in describing diseases more precisely interms of their symptom patterns.

3. Time-Based Synergy

Time-Based Synergy is the ability of the LB method to analyze the mannerin which a symptom varies over time in the patient, and to assign extradiagnostic weight to selected diseases based thereon. The way in which asymptom varies over time has great diagnostic significance. One exampleis pain over time. The concept of using word spectra with a series ofgraded adjectives has also been introduced, so that the words selectedby the patient indicate varying degrees of symptom intensity. Thissynergy type includes the general ability to use various aspects of asymptom time series to aid in, or refine a, diagnosis.

As described earlier, the symptom and valuator objects can be programmedwith functions that compute (or ask the patient for) various time-basedstatistics such as onset, offset, slope, trend, curvature, area, etc. Atrun time, when a script requires a time-based statistic for a givensymptom, the symptom object invokes its valuator object to compute them.Such computed values then become separate symptom values that can beweighted and scored like any other symptom values. Using this synergytype, the symptom/valuator object becomes a smart process that does notjust store symptom values, but that can perform dynamic, intelligent,internal analysis of how the symptom behaved over time in the patient,which generates useful diagnostic information in its own right.

The script author can distinguish or differentiate among candidatediseases based on when a symptom occurs in the patient, or on how thesymptom varies over time in the patient. The author can use the actualsymptom time variations to assign extra diagnostic weights to a disease.

One of the key features of the LB method is that it can use the timewhen symptoms occur and change to help diagnose the patient. Thisoutperforms many other automated diagnostic methods.

4. Onset/Offset Analysis Synergy

In one embodiment of the invention, onset/offset analysis synergy addsextra diagnostic weight to a disease if a given symptom exhibits onsetand/or offset in a specific manner. The type of onset and offset of asymptom can convey great diagnostic information. The followingdescription is for onset analysis synergy; a similar description appliesto offset analysis synergy.

At design time, the script author specifies for each disease:

(1) that a given symptom's onset is to be synergy weighted,

(2) the onset types that can be used to select added synergy weight,

(3) the synergy weights to be added depending on the onset type.

At run time, in the phase where the system is adding synergy weights tocandidate diseases, the system:

(1) detects that a given symptom's onset is to be synergy weighted,

(2) obtains the actual onset type for the symptom,

(3) compares the actual onset type to the pre-defined type,

(4) selects the onset synergy weight that corresponds to the actualtype,

(5) adds the selected onset synergy weight to the disease score.

Two examples of this synergy are as follows: 1) the sinusoidalrelationship of severity of pain in colic, 2) the “stuttering” start ofunstable angina.

5. Onset/Offset Slope Synergy

In one embodiment of the invention, onset/offset slope synergy addsextra diagnostic weight to a disease if a given symptom begins and risesto a maximum in a defined manner. The following description is for onsetsynergy; a similar description applies to the manner in which a symptomends, or its offset.

At design time, the script author specifies for each disease:

(1) that a given symptom's onset is to be synergy weighted,

(2) the onset slope threshold(s) to be used for selecting the synergyweight,

(3) the synergy weights to be added depending on the magnitude of theonset slope.

At run time, in the phase where the system is adding synergy weights tocandidate diseases, the system:

(1) detects that a given symptom's onset is to be synergy weighted,

(2) obtains the actual onset slope for the symptom,

(3) compares the actual onset slope to the pre-defined slopethreshold(s),

(4) selects the onset synergy weight that corresponds to the actualslope,

(5) adds the selected onset synergy weight to the disease score.

The nature of the onset (and offset) of a symptom can convey greatdiagnostic information. For example, a headache that starts suddenly andis very severe has more chance of being a sub-arachnoid hemorrhage thana severe headache that comes on gradually. In vascular events such as amyocardial infarction, the onset of pain is very sudden, that is, theslope of a line plotting severity versus time will be nearly vertical.The sudden onset of vomiting and diarrhea in Staph food poisoningcontrasts to other causes of gastroenteritis and food poisoning.

6. Onset/Offset Trend Synergy

In one embodiment of the invention, the onset (or offset) “trend” of asymptom refers to whether the symptom curve at that time point is linearor exponential, i.e., rising (or falling) at a constant rate or at anincreasing or decreasing rate. This is referred to as “linear orexponential”. The following description is for onset trend synergy; asimilar description applies to the manner in which a symptom ends, orits offset.

At design time, the author specifies for each disease:

(1) that a given symptom's onset's curve trend is to be synergyweighted,

(2) the onset trend threshold(s) to be used for selecting the synergyweight,

(3) the synergy weights to be added depending on the trend of the onsetslope.

At run time, in the phase where the system is adding synergy weights tocandidate diseases, the system:

(1) detects that a given symptom's onset trend is to be synergyweighted,

(2) obtains the actual onset trend for the symptom,

(3) compares the actual onset trend to the pre-defined trendthreshold(s),

(4) selects the onset synergy weight that corresponds to the actualtrend,

(5) adds the selected onset synergy weight to the disease score.

The shape of the onset (and offset) curve of a symptom can conveydiagnostic information. In one embodiment, the diagnostic system uses aRunge-Kutta curve-fitting algorithm to identify the type of curve underconsideration. Other algorithms are used in other embodiments.

7. Onset/Offset Symmetry Synergy

In one embodiment of the invention, onset/offset symmetry synergyassigns extra diagnostic weight if the onset and offset curves (or slopeif the relationship is linear) of a given symptom exhibit definedsymmetry characteristics.

At design time, the script author specifies for each disease:

(1) that a given symptom's onset and offsets are to be weighted forsymmetry,

(2) the parameters that define various symmetry relationships,

(3) the synergy weights to be added for a given symmetry relationship.

At run time, in the phase where the system is adding synergy weights tocandidate diseases, the system:

(1) detects that a given symptom's onset/offset symmetry is to besynergy weighted,

(2) obtains the actual onset and offset slopes and trends for thesymptom,

(3) converts the actual slopes and trends into pre-defined symmetryparameters,

(4) selects the symmetry synergy weight that corresponds to the actualdata,

(5) adds the selected weight to the disease score.

Onset and offset symmetry is important in making several diagnoses. Forexample, when a patient has a kidney stone that passes into the ureter(the tube connecting the kidney to the bladder), the patient experiencesthe sudden onset of very severe (and colicky) pain. In addition, whenthe stone passes into the bladder, the pain symptom frequentlydisappears as suddenly as it came on.

8. First Significant Symptom (FSS) Synergy

In one embodiment of the invention, first significant symptom (FSS)synergy assigns extra diagnostic weight to a disease if the patient'sFSS matches a list of possible FSSs for the disease. This synergyreflects the script author's real-world experience as to which symptomstend to be the first ones noticed by a patient.

At scripting time, a disease script author creates a special list ofsymptoms that a patient might notice first, and associates a weight witheach symptom. For example, for Appendicitis:

Anorexia 50 Nausea 30 Epigastric Pain 10

At run time, if the patient reports that Nausea was the first symptomshe or he noticed, the system will add 30 diagnostic points to thediagnosis of Appendicitis (and similarly add some weight to all otherdiseases that show Nausea in their FSS tables).

The key is that we use the information that the patient has a specificsymptom first to advance those diagnoses that match the patient. Wealready added points to the disease for just having this symptom; now weadd extra weight for it being first. That's the meaning of FSS synergy.

9. Simultaneous Synergy

In one embodiment of the invention, simultaneous synergy assigns extradiagnostic weight to a candidate disease if two or more of its symptomsare present in the patient over a given time period.

At design time, for each disease, the script author can define anynumber of special symptom combinations as well as an associateddiagnostic weight that should be added to the disease score if thecombination is present. The disease author may use a Gantt chart toappreciate the simultaneous, sequential and overlapping synergies.

At run time, for each disease, the system:

(1) tracks the symptoms actually present in the patient,

(2) determines if any pre-defined symptom combination is present, and—ifso—

(3) adds the associated weight to the score of the disease.

Simultaneous Synergy can be used very effectively by a script author todescribe a disease in terms of syndrome, and to characterize how varioussyndromes contribute to the disease.

10. Sequencing Synergy

In one embodiment of the invention, sequencing synergy assigns extradiagnostic weight to a candidate disease if two or more of its symptomsare present in the patient in a specific time sequence.

At design time, for each disease, the Script author may define anynumber of special symptom sequences, with associated diagnostic weightsto be added to the disease score if the patient exhibits the symptoms inthe specified order.

At run time, for each disease, the system:

(1) establishes an absolute start time for every symptom,

(2) tracks the symptoms actually present in the patient,

(3) detects if any author-defined sequence is present,

(4) determines whether the symptoms are present in the pre-defined timeorder,

(5) if appropriate, adds the sequence weight to the score of thedisease.

11. Overlapping Synergy

In one embodiment of the invention, overlapping synergy assigns extradiagnostic weight to a candidate disease if two or more of its symptomsare present in the patient at the same time for a specified amount oftime.

At design time, for each disease, the author can define any number ofspecial overlap symptom combinations, an overlap threshold, and adiagnostic weight that should be added to the disease score if thesymptom combinations overlapped in time for at least the specifiedthreshold time.

At run time, for each disease, the system:

(1) tracks the symptoms actually present in the patient,

(2) detects if any author-defined overlapping symptoms are present,

(3) computes for how long the symptoms overlapped in time,

(4) checks if the actual overlap meets or exceeds the specified overlapthreshold,

(5) if applicable, adds the specified overlap weight to the score of thedisease.

12. Integral (Area) Synergy

In one embodiment of the invention, integral synergy assigns extradiagnostic weight for the total amount of a symptom over a specifiedtime period. At design time, the author assigns diagnostic weight forthe amount of a symptom that a patient has reported over a timeinterval. At run time, the system (1) tracks the time chart of thesymptom values, (2) computes the total symptom value (i.e., integratesthe symptom curve) between two time points, and (3) if appropriate, addsan area synergy weight to the disease score.

The amount of a symptom over time gives information regarding biologicaland chemical body functions and reactions, which, in turn, havediagnostic value. An example is the amount of pain a patient hassuffered between two points in time. The integral synergy weight helpsthe system automatically recognize those patients that can benefit fromstrong analgesics, for example. In addition to diagnosis, this synergycould also be used for pain management. It could identify those patientswho perhaps cannot, or do not, identify themselves as needing narcoticanalgesics.

V. DESCRIPTIONS OF THE DRAWINGS

The software described by the following drawings is executed on astructure-based engine of a medical diagnostic and treatment advicesystem such as described in Applicant's U.S. Pat. No. 5,935,060. Oneembodiment of a structure-based engine is the list-based engine, butother embodiments may be implemented.

Referring now to FIG. 1, one embodiment of a diagnostic loop portion 100of a medical diagnostic and treatment advice (MDATA) system, that mayinclude a List-Based Engine (LBE), will be described in terms of itsmajor processing functions. Note that treatment advice may be optionallyprovided. However, the diagnostic aspect of this system is the mainfocus of the invention. Each function is further described with anassociated figure.

When the system starts, it assumes that another, off-line datapreparation program has prepared a suitable database of medicaldiagnostic data in the form of disease and symptom objects, DOs and SOsrespectively, and has assigned diagnostic weights to specific symptomvalues for each disease, and to special combinations or sequences ofsymptom values (called “synergies”). When a patient (who may access thesystem via a data communications network such as the Internet) presentsa medical complaint to the MDATA system to be diagnosed, the systemfirst retrieves all of the relevant disease objects from its databaseand assembles them into a candidate disease list. The system then usesthe diagnostic loop to develop a diagnostic profile of the candidatedisease list.

Inside the diagnostic loop, the system selects a current disease andsymptom to pursue. Then the system obtains the value of the symptom forthe current patient, calculates the weights associated with that value,and updates the scores of all affected candidate diseases with theweights. The updated scores are then used to re-rank the diseases and toselect the disease and symptom to be evaluated in the next iteration. Inthis manner, as the loop continues to iterate, the system builds up adiagnostic profile of the candidate diseases for the current patient.The loop can be interrupted at any point, and the then-currentdiagnostic profile examined in order to adjust the system parameters andcontinue the loop, or to terminate the loop, as desired. At the end ofthe loop, a diagnostic report is prepared that summarizes the actionstaken and the results computed.

The diagnostic loop 100 begins at state 102, where prior processing isassumed to have established a chief complaint for the current patientand a diagnostic report needs to be determined. Moving to function 110,the system acquires the computer resources required for the diagnosticloop. In this function, the system acquires computer memory as needed,creates required software objects, and sets variables to their initialvalues based on the current options, limits, and diagnostic goals. Thesystem also creates a list of diseases that are to be used as theinitial candidates for diagnosis. Moving to function 120, the systemselects one disease from the list of candidate diseases. This diseasebecomes the current focus disease, i.e., the disease for which a symptomis to be evaluated. Moving to function 130, the system selects onesymptom from the list of symptoms associated with the current disease.This symptom becomes the current focus symptom to be evaluated in thepatient. Moving to function 140, the system evaluates the currentsymptom in the patient by suitable means such as questioning thepatient, using logical inferences, mathematical computations, tablelookups, or statistical analysis involving other symptom values. Movingto function 150, the system updates all candidate diseases that use thecurrent symptom with the new symptom value obtained in function 140.Moving to function 160, the system updates all working lists and recordswith new values, scores, and diagnoses. Moving to function 170, thesystem reviews the progress of the diagnosis to decide whether anotheriteration of the diagnostic loop is required. Proceeding to a decisionstate 172, the system tests whether the diagnostic loop is to beterminated e.g., by user direction. If not, the system moves to function120 for another iteration; otherwise, the system moves to function 180,where the system saves appropriate values computed in the diagnosticloop and destroys all temporary data structures and objects required forthe diagnostic loop. Continuing at state 182, the system returns areport of the diagnostic results.

Referring now to FIG. 2, the Set-up Diagnostic Loop function 110previously shown in FIG. 1 will be described. Function 110 acquires thecomputer resources and sets up the data structures needed for thediagnostic loop 100 (FIG. 1). The system is designed to be fullyadaptive to its environment, and must initialize various memorystructures to prepare for processing. In an object-based embodiment,this preparation includes the creation of various objects. Each objecthas an initialization function that allows the object to initializeitself as needed.

Function 110 begins with entry state 202, where a chief complaint and adiagnostic mode have been established by prior processing. The chiefcomplaint will be used in state 212 to retrieve associated diseases. Thediagnostic mode will be used throughout function 110 to control detailprocessing. Moving to state 204, function 110 initializes the HAI/VAImode to either HAI or VAI, depending on the HAI/VAI mode desired forthis operation of the diagnostic loop. In HAI mode, the system willconsider all of the candidate diseases to select the next focus symptom;in VAI mode, the system will use only the list of symptoms of thecurrent focus disease.

Moving to state 206, function 110 initializes the Alternative Symptommode to either permit or inhibit alternative symptom valuation,depending on the Alternative Symptom mode desired for this operation ofthe diagnostic loop. If alternative symptoms are permitted, the systemwill later accept alternative values in place of the specified symptomvalue. If alternative symptoms are inhibited, the system will laterinsist on evaluating the specified symptom. Moving to state 208,function 110 initializes other internal variables that support the loopprocessing such as control flags, option indicators, loop limits, andloop goals. The exact variable and value of each variable depends on theparticular code embodiment chosen for the computer program. Moving tostate 210, function 110 obtains and initializes special computerresources required for this operation of the diagnostic loop. Thedetails of this initialization depend on the code embodiment chosen forthe computer program. For example, if an object is used to represent thelist of candidate diseases, state 210 creates and initializes an emptycandidate disease object; but if a relational table is used to representthe list of candidate diseases, state 210 creates and initializes anempty table to contain the candidate diseases. Moving to state 212,function 110 retrieves from the disease database all those diseases thatexhibit the chief complaint being diagnosed and (if available) thepatient's symptom time profile (FIG. 28).

As part of the off-line data gathering and preparation process, everydisease in the disease object database is associated with at least onechief complaint and with a time profile of symptoms. This association isused here to retrieve the list of diseases that constitute the initialcandidate set. By “candidate” disease is meant any human disease, as yetunidentified, that has some probability of being the patient's illness,based on the symptoms and the chief complaint indicated by the patient.Moving to a function 220, various internal working structures requiredto efficiently perform such detailed processing as sorting, searching,and selecting subsets of diseases and symptoms are initialized. Function220 is further described in conjunction with FIG. 3. Moving to state222, function 110 returns control to the process that called it, ineffect moving to function 120 of FIG. 1.

Referring now to FIG. 3, the Set-up Disease-Symptom Structure function220, which sets up the candidate disease list and actual symptom datastructures to be used in the diagnostic loop, will be described. Thecandidate disease list is generated and then partitioned into threesublists of urgent, serious, and common diseases. This separation allowsthe system to consider candidate diseases in order of urgency andseriousness before it considers the common diseases.

Function 220 begins with an entry state 302. Moving to state 304,function 220 creates a disease-symptom matrix (DSM), which is a datastructure with columns for all the diseases selected by the chiefcomplaint, rows for the maximum number of symptoms used by all candidatediseases, and time slices (Z-axis) for the time intervals used by thediseases. Other disease-symptom structures may be used in otherembodiments. Moving to state 306, function 220 extracts all diseasesmarked ‘urgent’ from the candidate diseases and sorts these bydecreasing urgency. Moving to state 308, function 220 places the mosturgent disease as the leftmost column of a Disease-Symptom Matrix (DSM)[which can be considered one time slice of a Disease-Symptom Cube(DSC)]. Moving to state 310, function 220 places the remaining urgentdiseases in the next columns of the DSM. Moving to state 312, function220 extracts all diseases marked ‘serious’ from the candidate diseaselist; sorts these serious diseases by decreasing seriousness; places themost serious disease as the next available leftmost column of the DSM,next to the urgent diseases; and places the remaining serious diseasesinto the next columns of the DSM. Moving to state 314, function 220sorts the candidate diseases that remain after the urgent and seriousdiseases were removed by decreasing prevalence, i.e., the probability ofoccurrence of the disease in the population from which the patientcomes, and places the remaining diseases in order of decreasingprevalence as the next available leftmost column of the DSM, next to theserious diseases. Moving to state 316, function 220 returns control tothe process that called it, in effect to state 222 of FIG. 2.

Referring now to FIG. 4, the Pick Current Disease function 120, in whichthe candidate disease list is searched to select one disease as thecurrent focus disease, will be described. The selection criteria can beany computation that can identify diseases with high potential for beingthe actual disease of the patient, such as selecting diseases based ontheir performance so far in the current diagnostic session, on highdiagnostic score, high rate of change of score (diagnostic momentum),number of positive responses to questions, or best statistical match ofdisease timelines, which may happen in the HAI mode. In anotherselection mode (VAI), an external user or process has already selectedthe focus disease, so that the system has no choice.

Function 120 begins with the entry state 402, where a list of diseasesexists that are candidates for diagnosis. Function 120 selects one ofthese candidate diseases to become the current focus disease. Theselection can be made using one of many rules; which rule is useddepends on the diagnostic mode. FIG. 4 shows two rules being tested, butany number of rules can be added. Moving to a decision state 404,function 120 first checks whether the candidate disease list is empty.If so, there are no more diseases to examine, and function 120 moves tostate 440. At state 440, function 120 sets an outcome signal to indicatethat no current disease has been selected and then moves to state 434,where the process returns. If the candidate list is not empty atdecision state 404, then there is at least one candidate diseaseremaining and function 120 moves to a decision state 406 to test whetherall candidate diseases have been processed. If so, function 120 moves tostate 442, where it sets an outcome signal to that effect and moves tostate 434 to return control. However, if, at decision state 406, somediseases remain to be processed, function 120 moves to a decision state408. At decision state 408, if the selection mode is set to VAI, i.e.,forced to use a specific disease, function 120 moves to a decision state410, otherwise to state 412. At state 410, if the VAI disease has notyet been diagnosed, function 120 moves to state 430, selects the VAIdisease as the current disease and moves to state 432. But if, atdecision state 410, if the disease that was pre-selected has alreadybeen diagnosed, function 120 moves to state 412, to reset processing tothe HAI mode.

With state 414 begin the states of actually selecting a disease. Thediagnostic mode that is in effect when the diagnostic loop startsspecifies or implies a disease selection rule or criterion. This rule isbased on either the actual diagnostic progress so far, or the potentialprogress that could be made, using such diagnostic measures asweighting, momentum, score, and probability. This selection rule can bechanged by internal processing or external request, but some selectionrule will always be in effect. Function 120 uses the rule to select oneof the candidate diseases as focus disease. Moving to a decision state414, if the selection rule is to select the candidate disease with thehighest actual diagnostic momentum, function 120 continues to state 416.At state 416, function 120 selects the candidate disease with thehighest current diagnostic momentum and then moves to state 432. But if,at decision state 414, the current rule is otherwise, function 120 movesto a decision state 418.

At decision state 418, if the selection criterion is to select thecandidate disease with the highest potential diagnostic momentum,function 120 moves to state 420, selects the candidate disease with thehighest potential diagnostic momentum and then moves to state 432. Butif, at decision state 418, the current diagnostic mode is otherwise,function 120 moves to a decision state 422. At decision state 422, ifthe diagnostic mode is to select the candidate disease using some othercriterion such as time profile matching or direct patient input,function 120 moves to state 424, which uses some other criterion in asimilar manner to select the disease and then moves to state 432. Butif, at decision state 422, there are no more criteria to be used,function 120 moves to state 426, applies the default rule that maysimply select the next eligible candidate disease and then moves tostate 432. At state 432, function 120 sets an outcome signal to indicatethat a current focus disease has been selected and then function movesto state 434 to return the outcome signal and the current diseaseidentifier to the process that called function 120, in effect tofunction 130 of FIG. 1.

Referring now to FIG. 5, the Pick Current Symptom function 130, whichselects a symptom of the current focus disease to become the nextcurrent focus symptom, will be described. Here, the system examines thelist of symptoms of the current focus disease and uses various criteriato select one of them as the next focus symptom. The goal is to select asymptom that will advance the diagnostic score with the least system orpatient effort, which can be achieved in a number of ways such as byselecting a symptom for which the value has already been obtained byanother disease, or a symptom that has been specially identified by theauthor, or a symptom with high diagnostic weight, or one that is likelyto rule the disease in or out at once, or symptoms that have highcommonality across diseases. Once function 130 has selected a symptom inthis manner, it will also select all symptoms that have been identifiedas acceptable alternatives by the author.

Function 130 begins with the entry state 502, where a current focusdisease has been selected and the function must now select from thatdisease's symptom list a current focus symptom, plus possibly one ormore alternative symptoms, if any were specified by the author. Thesymptom can be selected using one of many rules. Which rule is useddepends on the diagnostic mode. Moving to a decision state 504, function130 first checks whether there are any symptoms remaining in the currentdisease that have not yet been evaluated for the current patient. If so,function 130 moves to state 506, which returns an outcome signal thatindicates that no current symptom has been selected. But if, at decisionstate 504, there is at least one eligible symptom, function 130 moves toa decision state 508.

Decision state 508 begins the actual selection of a symptom. Thediagnostic mode that is in effect when the diagnostic loop starts canspecify or imply one of many symptom selection rules or criteria, whichcan be changed by internal processing or external request. However, inone embodiment, some symptom selection rule will always be in effect. Inaddition, for any given symptom, a disease can identify one or morealternative symptoms that can be used in its place. The focus symptomreturned by state 130 can thus consist of a symptom package thatcontains at least one symptom plus zero or more alternative symptoms. Atdecision state 508, function 130 tests whether the disease has symptomsthat must be evaluated before other symptoms of that disease. Suchsymptoms serve to quickly eliminate a disease that does not meet basiccriteria. If the disease has such initializing symptoms, function 130moves to a decision state 510. At decision state 510, if all of theinitial symptoms have been evaluated, function 130 moves to a decisionstate 516. But if, at decision state 510, there are any unevaluatedinitial symptoms, function 130 moves to state 512, selects the nextinitial symptom as focus symptom and moves to return state 514. Movingto decision state 516, if the current diagnostic mode specifiesselecting symptoms with the largest diagnostic weight, function 130moves to state 518, selects the symptom with the largest diagnosticweight and moves to return state 514. But if, at decision state 516, therule is not to select the symptom with the largest weight, function 130moves to a decision state 520. At decision state 520, function 130accommodates symptoms that are related to each other in some way such asgroups, combinations, or sequences. If the current diagnostic modeindicates that related symptoms are to be considered, function 130 movesto state 522, but if related symptoms are not to be considered, function130 moves to state 526.

At decision state 522, if there is a symptom related to the previouslyevaluated symptom, function 130 moves to state 524. At state 524,function 130 selects a symptom that is related to the previouslyevaluated symptom and moves to return state 514. But if, at decisionstate 522, there is no related symptom, function 130 moves to state 526.At decision state 526, if the current diagnostic mode indicates thatsymptoms are to be considered that are easiest to evaluate, function 130moves to state 528, selects the next symptom that is easiest to evaluateand then moves to return state 514. But if, at decision state 526, therule is not to select the easiest symptom, function 130 moves to adecision state 530. At decision state 530, if the current diagnosticmode indicates that symptoms are to be selected at random, function 130moves to state 532, selects the next symptom at random from the currentdisease's symptom list and then moves to return state 514. But if, atdecision state 530, the rule is not to select a random symptom, function130 moves to state 534, selects the next eligible symptom from thecurrent disease's symptom list and then moves to return state 514. Atstate 514, function 130 returns the current focus symptom (and allalternative symptoms, if any) to the process that called function 130,in effect to function 140 of FIG. 1.

Referring now to FIG. 6, the Obtain Symptom Value function 140, whichevaluates the current focus symptom, i.e., establishes a specific valuethat occurred or existed at some time t in the patient, will bedescribed. At this point in the diagnostic loop, the system has selecteda current focus symptom and must now determine its value at some time tin the on-line patient. Symptom values can be simple (e.g., patient is asmoker) or detailed (e.g., patient has a 12-year, 2-packs/day smokinghistory); values can be simple numbers or symbols, or complex graphics,photos, or disease timelines (see, for example, FIG. 28).

Function 140 must now select either the current symptom or one of itsalternatives, and then obtain its value in the patient at specifictimes. If the current diagnostic mode permits the use of alternativesymptoms, and the current focus symptom has an alternative symptom thathas already been evaluated, then the value of that alternative symptomis used at once, without further evaluation. The time saved by thisevaluation shortcut is the basic reason for the use of alternativesymptoms.

How the value itself is obtained depends on the symptom being evaluatedand can use many different methods such as reviewing the patient'smedical record, asking the on-line patient direct questions, drawinglogical inferences from other symptom values, using mathematical andstatistical formulas, using specially prepared lookup tables, evenhaving the patient perform self-examinations. These different evaluationtechniques are described here collectively as a ‘valuator function’.

Function 140 begins with the entry state 602, where a current focusdisease and symptom have been selected by prior processing. Moving to adecision state 604, function 140 first checks whether an acceptablevalue was already obtained during this session, perhaps by some otherdisease or some acceptable alternative symptom. If the current focussymptom already has a value, function 140 moves to state 606 to returnthat value; otherwise, function 140 moves to a decision state 608. Atdecision state 608, function 140 checks whether the current symptomalready has a value in the patient's medical record. If so, function 140moves to state 606 to return that value; otherwise, function 140 movesto a decision state 609. At decision state 609, if the current symptomhas alternative symptoms and the mode permits their use, function 140moves to a decision state 610; otherwise function 140 moves to adecision state 612. At decision state 610, if the current symptom has anacceptable alternative symptom value then function 140 skips furtherevaluation and returns the alternative value at time t from state 611 atonce at state 606; otherwise function 140 moves to decision state 612.

At decision 612, function 140 begins the process of evaluating thecurrent symptom by determining the valuator type of the current symptom.If the valuator type is a direct question, function 140 moves tofunction 620 to ask questions of the on-line patient, which is describedin FIG. 7 and then moves to state 606. But if, at decision state 612,the valuator type is a mathematical formula, function 140 moves tofunction 630 for evaluating the formula (described in FIG. 8) and thenmoves to state 606. But if, at decision state 612, the valuator type isa table lookup, function 140 moves to function 640 to look up the valuein a table (described in FIG. 9), and then moves to state 606. But if,at decision state 612, the symptom value is based on analyzing aspectrum of terms, function 140 moves to function 650 to perform thespectrum of terms analysis and obtain a value, which is described inFIG. 10. Then function 140 moves to state 606. Finally, if at decisionstate 612 the valuator is of some other type, such as disease timeprofile matching (FIG. 28), function 140 moves to state 660 to invokethat valuator and obtain a value in a similar manner. Then function 140moves to state 606 to return the current symptom and its value at sometime t to the calling process that invoked function 140, in effect tofunction 150 of FIG. 1.

Referring now to FIG. 7, the Question Valuator function 620, which ispart of a question valuator object, will be described. A questionvaluator object obtains a symptom value at time t by asking an on-linepatient one or more questions. To ask the questions, it uses one or morenode objects that are pre-programmed by the script author to communicatewith the patient in some natural language, using appropriateinstructions, definitions, explanations, questions, and responsechoices. The response selected by the patient is encoded as a symptomvalue and ultimately returned to the caller of function 620.

Function 620 begins with entry state 702, where the current focusdisease, symptom, and question objects have been established by priorprocessing and are given to the function. Function 620 now questions theon-line patient to obtain a value for the symptom at some time t. Movingto state 706, function 620 retrieves, from a database of node objects, aset of nodes listed in the current valuator object. Moving to state 708,function 620 displays the next node, which includes instructions and aquestion, to the patient. Moving to a decision state 710, if there is noresponse within a prescribed time, function 620 moves to return state712 and returns a signal that the question object has timed out. But if,at decision state 710, function 620 obtains a response from the patient,the function moves to a decision state 714. At decision state 714,function 620 determines whether the response is a final symptom value ora signal to activate another node. If the response activates anothernode, function 620 moves back to state 708, which repeats the sequenceof question and response states with a different node. Over time, theeffect of this sequence is to generate a dialog with the patient thatculminates in a symptom value being generated at state 714. When, atdecision state 714, the patient's response is a value, function 620moves to state 716 and returns the value obtained from the patient.

Referring now to FIG. 8a, the Formula Valuator function 630, which ispart of a formula valuator object, will be described. A formula valuatorobject computes the value of a symptom at some time t by evaluating agiven formula. In the object-based embodiment, each formula is embeddedin a different formula valuator object, and there are as many differentformula valuator objects in the system as there are formulas. Any otherobject that needs to evaluate a formula can call the appropriate formulavaluator. A simple example is to convert an absolute date such as Dec.7, 1941 into an age in years at some later time t.

Function 630 begins with entry state 802, where current focus disease,symptom, and valuator objects have been selected by prior processing.Function 630 now evaluates the formula to obtain a value for thesymptom. Moving to function 810, the formula calculator object isinvoked. Moving to state 812, function 630 returns the computed value.

Referring now to FIG. 8b, the Execute Formula Valuator function 810,which evaluates a given formula that may use other symptom values tocompute a symptom value at time t. The formula is given to function 810as a set of suitably encoded operations and operands in some formalizedsystem of mathematics such as arithmetic, geometry, trigonometry,algebra, calculus, probability, or statistics. Function 810 performs therequired operations and returns the computed value. One specialsub-processing case occurs when an operand of the formula is itself asymptom that still needs to be evaluated, in which case function 810interrupts the evaluation, causes the operand symptom to be evaluated,and then continues the evaluation of the formula. This is a potentiallyrecursive process, since the evaluation of a symptom can itself involvethe evaluation of a different formula. By using this design, anystructure of nested formulas, using any symptom objects can be specifiedby a script author and evaluated when needed.

Function 810 begins with the entry state 820, where the formula and itsarguments are known. Moving to state 822, the formula is evaluated asfar as is possible with the given arguments. In some cases, this willcompletely evaluate the formula; in others, it will encounter anargument that is itself a symptom that still needs to be evaluated.Moving to a decision state 824, if an argument requires furtherevaluation, function 810 moves to the Execute Symptom Object function140; otherwise it moves to state 832. At function 140, the argumentsymptom is evaluated by calling the appropriate symptom object (FIG. 6);then function 810 moves back to state 822 to continue evaluating theformula. At state 832, function 810 continues to evaluate the formulauntil the final value has been computed. Moving to state 834, the finalvalue is returned.

Referring now to FIG. 9, the Lookup Valuator function 640, which is partof a lookup valuator object, will be described. A lookup valuator objectcomputes the value at time t of a symptom by looking it up in a table orchart. It is often the case that the value of a symptom is known to thesystem indirectly, perhaps from some other context in which a chart ortable was prepared. One simple example is a time-based temperature chartthat can be used to retrieve the value of fever at time t.Alternatively, function 640 could use statistical computations based ona chart, such as counting certain occurrences, finding the area under adisease timeline between two given times, or matching disease timelines.

Function 640 begins with entry state 902, where a symptom has beenselected for evaluation. Moving to state 904, the symptom is looked upin a prepared lookup medium such as a graph, chart, or database table.Moving to state 906, function 640 returns the value to the process thatcalled function 640. Referring now to FIG. 10, the Spectrum of TermsValuator function 650, which is used for symptoms that depend on thepatient's subjective description, will be described. Function 650converts a patient's subjective description of a symptom at time t intoa specially encoded token that is returned by the function and processedby the system like any other value. Function 650 provides a list of keydescriptor words to the patient, lets the patient select one or morewords, and encodes the selected words into a token that is returned.

States 1004 and 1006 of the figure show that the spectrum of descriptorterms is prepared, and weights are assigned to the various terms in anoff-line preparation process of the symptom object. These data aretypically prepared and stored in a database that is accessed duringon-line diagnosis.

In the on-line diagnostic system, function 650 begins at state 1008,where the function presents the spectrum of terms to the patient in somemanner that allows the patient to select a set of descriptive terms fortime t. Moving to state 1010, function 650 obtains and processes theterm(s) selected by the patient. Moving to a decision state 1014, ifother aspects of the symptom are to be processed, function 650 moves tostate 1016; otherwise the function moves to state 1018. At state 1016,function 650 prepares to process the next aspect of the symptom's termspectrum and then moves back to state 1008. At state 1018, function 650collects and saves the terms collected for time t into a suitable codethat can be returned as a value. Moving to state 1020, function 650encodes the terms collected from the patient and returns them as thevalue to the process that called function 650.

Referring now to FIG. 11, the Apply Symptom Value function 150, whichreceives a symptom value that has just been elicited or computed andapplies it and its effects to various candidate diseases, will bedescribed. In one embodiment, using a central system, function 150 loopsthrough the candidate disease list and applies to each disease D theeffects of the new value. For each disease D, it retrieves theappropriate diagnostic weights, computes the applicable synergisticweights, computes the applicable synergy weights, and notes any othereffects mandated by the disease author, such as mandatory score changes,disease status changes due to rule-ins and rule-outs, and changesrequired to handle value changes of symptoms that are acting asalternative symptoms in other diseases.

In an alternative, object-based embodiment, each disease object has abuilt-in method that processes new symptom values; and function 150calls that method to “notify” each disease object of the new symptomvalue. Each disease object is programmed to apply the effects of the newvalue to its own data, which simplifies the handling of certain highlydisease-specific updating rules. However, in either embodiment, the samelogical processing takes place.

Note, in one embodiment, that the diagnostic weight changes computed asa result of the new value are merely saved, but are not added to thediagnostic scores until after all candidate disease changes have beencomputed and all diseases can be updated in unison (see FIG. 21). Thisis necessary to prevent changes in scores, rulings, alternative symptomweights, and synergistic effects of diseases near the beginning of thecandidate list from influencing and distorting computations for diseasesfurther down the list. Computing score changes and incrementing scoresis a two-pass process to insure correct advancing of all diseases scoresas a single generation.

Function 150 begins with entry state 1102, where a new symptom value hasbeen computed and must be applied to all candidate diseases that usethis symptom. Moving to state 1104, function 150 initiates a loop thatprocesses every disease D in the candidate disease list. Moving to adecision state 1106, if disease D does not use the new symptom, function150 ignores disease D and moves to a decision state 1122 for anotheriteration of the loop. But if, at decision state 1106, disease D doesuse the current symptom, function 150 moves to state 1108 to process it.At state 1108, function 150 retrieves, from the weights table fordisease D, the diagnostic weight specified for the value of the currentsymptom at time t. This new weight is stored in the disease object for Dfor later processing.

Proceeding to state 1110, if the diagnostic weighting for the currentsymptom depends on analyzing incremental changes in symptom values overa time interval, function 150 computes the effect of the changes,retrieves a corresponding weight, and saves it for later updating.Advancing to a Compute Synergies function 1120, the impact of the newsymptom value on disease D is computed, as further described inconjunction with FIG. 12. Moving to decision state 1122, function 150checks if there are more candidate diseases to be processed. If so,function 150 moves to state 1124, increments the loop index D and movesback to state 1106 to start another iteration of the loop. But if, atdecision state 1122, all candidate diseases have been processed,function 150 moves to state 1126 and returns to the calling process.

Referring now to FIG. 12, the Compute Synergies function 1120 (used inFIG. 11), which computes the synergistic weight, if any, of a givensymptom value, will be described. Synergy here refers to specialpredefined qualities of symptoms as well as time-based relationships andinteractions among different symptoms, which often have a significantdiagnostic impact. Whereas the use of diagnostic weights for simplesymptom values is a first-order effect, the use of time-basedsynergistic values is a second-order effect, a mathematical“fine-tuning” that helps to differentiate competing diagnoses anddistinguishes the MDATA system from other automated diagnostic systems.Note that only a few of the major synergy types are shown; there aremany possible synergy types that can all be analyzed in a similar manneras shown here.

Function 1120 begins with entry state 1202, where a new symptom valuehas been computed and must now be applied to all synergistic symptomsfor a given disease. Moving to a decision state 1204, function 1120tests whether the given disease has any symptoms that involve synergies.If not, function 1120 returns at state 1206; otherwise, function 1120moves to state 1208 and initializes a loop that processes every synergyi defined for the given disease. Continuing at a decision state 1210,function 1120 obtains the next synergy i of the disease and reviews itstype. Depending on the type of synergy i, function 1120 computes thesynergy as follows:

If the type of synergy i is First Significant Synergy, function 1120moves to function 1220 to compute the FSS Synergy, which is furtherdescribed in conjunction with FIG. 13. Then function 1120 moves to adecision state 1272. If, at decision state 1210, the synergy type isOnset or Offset Synergy, function 1120 moves to function 1230 to computethe Onset or Offset Synergy, which is further described in conjunctionwith FIG. 14. Then function 1120 moves to decision state 1272. If, atdecision state 1210, the synergy type is Sequencing Synergy, function1120 moves to function 1240 to compute the Sequencing Synergy, which isfurther described in conjunction with FIG. 18. Then function 1120 movesto decision state 1272. If, at decision state 1210, the synergy type isSimultaneous Synergy, function 1120 moves to function 1250 to computethe Simultaneous Synergy, which is further described in conjunction withFIG. 19. Then function 1120 moves to decision state 1272. If, atdecision state 1210, the synergy type is Time Profile Synergy, function1120 moves to function 1260 to compute the Time Profile Synergy, whichis further described in conjunction with FIG. 20. Then function 1120moves to decision state 1272. If, at decision state 1210, the synergytype is some other synergy, function 1120 moves to state 1270 to computethe synergy. State 1270 is intended to show that there may be many othersymptom combinations that exhibit synergy, which would be computed inlike manner as functions 1220 through 1260. After any one synergysymptom has been computed, function 1120 moves to decision state 1272and checks whether there are more synergies to process. If so, function1120 moves to state 1274 where it increments the index that selects thenext synergy and then moves back to state 1210 to initiate anotheriteration of the loop. But if, at decision state 1272, there are no moresynergies to compute, function 1120 moves to state 1276 and returns tothe calling process.

Referring now to FIG. 13, the Calculate First Significant Symptom (FSS)function 1220 (FIG. 12) will be described. The FSS function 1220determines if a given actual symptom value belongs to the firstsignificant symptom of a given disease in order to add extra diagnosticweight to that disease. The meaning of “first significant symptom” isillustrated in and described in conjunction with FIG. 28 as the earliestsymptom in the disease process.

Function 1220 begins with entry state 1302, where a new value has beencomputed for a symptom and function 1220 must now retrieve the extradiagnostic weight associated with the symptom being the FirstSignificant Symptom of the disease. Moving to a decision state 1304,function 1220 tests whether the given disease identifies any firstsignificant symptoms. If not, function 1220 returns at once at state1306, otherwise function 1220 moves to a decision state 1308 and checkswhether the given symptom is identified as a First Significant Symptomof the given disease's timeline. If not, function 1220 returns at onceat state 1306, otherwise function 1220 moves to state 1310. At state1310, function 1220 retrieves the diagnostic weight specified for thegiven symptom value for the disease and moves to state 1312. At state1312, function 1220 saves the diagnostic weight, moves to state 1306 andreturns to function 1120 (FIG. 12).

Referring now to FIG. 14, the Calculate Onset [Offset] Synergy function1230 (used in FIG. 12) will be described. Function 1230 analyzes if andhow the values of a given symptom exhibit special onset (or offset)characteristics that have medical significance and thus add extradiagnostic weight to the disease.

Function 1230 begins with entry state 1402, where a new value has beencomputed for a symptom and the function must now retrieve the extradiagnostic weight associated with special onset and offset values of thegiven symptom. Moving to a decision state 1404, function 1230 testswhether the disease uses onset/offset analysis and whether it identifiesthe given symptom value as a special onset or offset value. If the givensymptom does not use onset/offset analysis, function 1230 moves to state1416 to return at once (at state 1416); otherwise it moves to function1410 a. At function 1410 a, the onset or offset synergy of the newsymptom value is analyzed (as described at FIG. 15) and then moves to adecision state 1414. At decision state 1414, function 1230 checkswhether its previous processing has changed any onset or offset values.If so, the function moves to function 1410 b, or otherwise to state1416. At function 1410 b, the symmetry between onset and offset isanalyzed and synergy weight is assigned (as described at FIG. 15). Atthe completion of function 1410 b, function 1230 moves to state 1416 andreturns to the calling function 1120 (FIG. 12).

Referring now to FIG. 15, function 1410, which analyzes the onset andoffset values of a given symptom and determines their characteristicswith respect to time, will be described. Function 1410 includes aportion 1410 a to analyze an onset or offset synergy and a portion 1410b to analyze symmetry synergy.

Function 1410 begins with entry state 1502, where the new symptom valuesare given. Moving to a decision state 1504, if the given values are notrelated to onset or offset, the function returns a “no data” signal atdecision state 1504; otherwise function 1410 can perform portion 1410 ato analyze an onset or offset synergy and moves to a decision state1508. At decision state 1508, if there are new values related to symptomonset, function 1410 moves to function 1510, or otherwise to a decisionstate 1522. At function 1510, the diagnostic weight of the slope ofsymptom onset with respect to time is computed as further described inFIG. 16. Moving to function 1520, the diagnostic weight of the trend,i.e., the change of slope of symptom onset with respect to time iscomputed, as further described in FIG. 17. Moving to decision state1522, if there are new offset values, function 1410 moves to function1510′, otherwise function 1410 returns at state 1528. At function 1510′,diagnostic weight of the slope of symptom offset with respect to time iscomputed, as further described in FIG. 16. Moving to function 1520′, thediagnostic weight of the trend, i.e., the change of slope of symptomoffset with respect to time is computed, as further described in FIG.17. Moving to a decision state 1524, if there are both onset and offsetvalues in the symptom, function 1410 can perform portion 1410 b toanalyze symmetry synergy and proceeds to state 1526; otherwise function1410 returns at state 1528. At state 1526, the function portion 1410 bcomputes the diagnostic weight of the symptom onset/offset symmetry.Moving to state 1528, function 1410 returns to the calling function 1230(FIG. 14).

Referring now to FIG. 16, function 1510, which computes the diagnosticweight of onset and offset slopes for a symptom, will be described. Thisdescription is for symptom onset (1510); a similar description appliesfor symptom offset (1510′). Function 1510 begins with entry state 1602,where new symptom values are given. Moving to a function 2500, the slopewith respect to time of the given onset or offset value is computed, asfurther described in conjunction with FIG. 25. Moving to a decisionstate 1606, if no valid slope was returned by function 2500, function1510 returns at state 1614; otherwise it moves to a decision state 1608.At decision state 1608, if the onset slope does not reach or exceed theonset slope threshold, function 1510 returns at state 1614; otherwisefunction 1510 moves to state 1610. At state 1610, function 1510retrieves the weight assigned to the onset slope value for the givensymptom and saves it for later analysis in the disease object. Moving tostate 1614, function 1510 returns to function 1410 (FIG. 15).

Referring now to FIG. 17, function 1520, which computes the diagnosticweight of onset (1520) and offset (1520′) trends for a symptom, will bedescribed. The speed with which a symptom begins or ends in a patienthas diagnostic significance, which is determined and weighted by thisfunction. This description is for symptom onset trend (1520); a similardescription applies for symptom offset trend (1520′). Function 1520begins with entry state 1702, where new symptom values are given. Movingto a function 2600, the trend with respect to time of the given onsetvalue is computed, as further described in conjunction with FIG. 26.Moving to a decision state 1706, if no valid trend was returned byfunction 2600, function 1520 returns at state 1714; otherwise it movesto a decision state 1708. At decision state 1708, if the onset trend isless than the onset trend threshold, function 1520 returns at state1714; otherwise function 1520 moves to state 1710. At state 1710,function 1520 retrieves the weight assigned to the onset trend value andsaves it for later analysis in the disease object. Moving to state 1714,function 1520 returns to the caller function 1410 (FIG. 15).

Referring now to FIG. 18, the Calculate Sequence Synergy function 1240(FIG. 12) will be described. Function 1240 checks whether a specific,author-defined sequence of symptom values occurred in the patient beingdiagnosed for a given disease. If so, function 1240 retrieves the extradiagnostic synergy weight associated with that special symptom sequenceand saves it for later analysis.

Function 1240 begins with entry state 1802, where a disease and asymptom value at some time t is given to the function. Moving to adecision state 1804, function 1240 tests whether the disease authordefined any sequence synergy weighting at all. If so, function 1240moves to state 1806, otherwise it returns at once at return state 1814.At state 1806, function 1240 retrieves from the disease object allsymptom values that are involved in the sequence synergy computation.Moving to state 1808, function 1240 compares the time sequence ofsymptom values actually reported in the patient to the author's timesequence of symptoms. Moving to a decision state 1810, if the patient'ssymptom sequence matches the author's, function 1240 moves to state1812; otherwise the function returns at state 1814. At state 1812,function 1240 retrieves the diagnostic weight associated with thesymptom time sequence from the given disease's weight table and savesthe weight with any other actual weights of the given disease. Moving tostate 1814, function 1240 returns to its caller function 1120 (FIG. 12).

Referring now to FIG. 19, the Calculate Simultaneous Synergy function1250 (FIG. 12) will be described. Function 1250 checks whether aspecific, author-defined set of symptom values occurred at the same timeor over the same time period in the patient being diagnosed for a givendisease. If so, function 1250 retrieves the extra diagnostic weightassociated with that special set of simultaneous symptoms and adds it tothe list of actual diagnostic weights of the disease.

Function 1250 begins with entry state 1902, where a disease and asymptom value at some time t is given to the function. Moving to adecision state 1904, function 1250 tests whether the disease authordefined any simultaneous synergy weighting at all. If so, function 1250moves to state 1906, otherwise it returns at once at return state 1912.At state 1906, function 1250 retrieves from the disease object allsymptom values that are involved in the simultaneous synergycomputation. Moving to a decision state 1908, if the patient's symptomset at time t matches the author's pre-defined symptom set, function1250 moves to state 1910; otherwise the function returns at state 1912.At state 1910, function 1250 retrieves the diagnostic weight associatedwith the simultaneous symptom set from the given disease's weight tableand saves the weight with any other actual weights of the given disease.Moving to state 1912, function 1250 returns to its caller function 1120(FIG. 12).

Referring now to FIG. 20, the Calculate Timeline Profile Synergyfunction 1260 (FIG. 12) will be described. Function 1260 checks whetherthe patient being diagnosed for a given disease has reported a symptomtime profile (FIG. 28) or individual symptom values occurring at time tsuch that they match or “fit into” a specific, author-defined diseasetime profile or disease timeline. If the given symptom occurred in thepatient with predefined values at times that correspond to a diseasetime profile defined by the author, function 1260 retrieves the extradiagnostic weight that the author associated with the symptom timeprofile and adds it to the list of actual diagnostic weights of thedisease.

Function 1260 begins with entry state 2002, where a disease and asymptom value at time t is given to the function. Moving to a decisionstate 2004, function 1260 tests whether the disease author defined anysymptom time profile weights at all. If so, function 1260 moves to state2006, otherwise it returns at once at return state 2012. At state 2006,function 1260 retrieves from the disease object the symptom time profilethat is involved in the computation. Moving to a decision state 2008, ifthe patient's symptom time profile matches the author-defined timeprofile, function 1260 moves to state 2010; otherwise the functionreturns at state 2012. At state 2010, function 1260 retrieves thediagnostic weight associated with the time profile from the givendisease's weight table and saves the weight with any other actualweights of the given disease. Moving to state 2012, function 1260returns to its caller function 1120 (FIG. 12).

Referring now to FIG. 21, the Update and Record function 160 (FIG. 1)will be described. Prior to entry into Function 160, the diagnostic loop100 has just recomputed the diagnostic weights of all candidate diseasesbased on some new value for the current focus symptom. Function 160 nowallows each candidate disease to take one small incremental diagnosticstep, based on the new value. Function 160 updates the diagnosticmomentum, score, and status of each candidate disease, and modifies andreviews their diagnostic ranking. If this latest step causes one or morediseases to reach a diagnostic decision point, function 160 processesthat decision. Function 160 then prepares the candidate disease list foranother iteration of the diagnostic loop.

Function 160 begins with entry state 2102, where new weights have beenestablished in all candidate diseases that use the current symptom.Moving to state 2104, function 160 initializes a loop to process eachdisease in the candidate disease list in turn, as disease D. Moving to adecision state 2106, if disease D does not use the current symptom, thenit cannot have been affected by the latest weight changes, so function160 skips the rest of the loop and moves to a decision state 2118. Butif, at decision state 2106, disease D does use the current symptom, thenfunction 160 moves to state 2108. At state 2108, function 160 sums allof the diagnostic weights added by the new symptom value D. Thisincludes the basic weight of the symptom value and all extra,incremental weights added by the various synergy functions. Moving tostate 2110, function 160 computes the diagnostic momentum of disease D,which, in one embodiment, is simply the sum computed at state 2108. Thismomentum is the incremental diagnostic progress made by disease D forthe current question. It is saved and used in another context toevaluate how fast disease D is advancing compared to other candidatediseases. Moving to state 2112, function 160 updates the diagnosticscore of disease D by adding to it the momentum computed in state 2110.

Moving to state 2114, function 160 reviews and adjusts the diagnosticstatus of disease D. Function 160 compares the new diagnostic score tovarious author-defined thresholds that signal status changes such asruling disease D ‘in’ or ‘out’, or changing the diagnostic rank ofdisease D so that it will receive more or less attention in the nextiteration of the diagnostic loop. Moving to state 2116, function 160updates various working lists and databases to record the actions anddecisions taken with respect to disease D. Moving to decision state2118, if there are more candidate diseases to be processed, function 160moves to state 2120 which increments the loop index D and then movesback to state 2106 which starts another iteration. But if, at decisionstate 2118, there are no more diseases to be processed, function 160moves to state 2122 and returns to the caller, which in this case isfunction 170 of FIG. 1.

Referring now to FIG. 22, the Review Diagnoses function 170 as used inthe diagnostic loop (FIG. 1) will be described. At the entry to function170, the system has just updated all candidate diseases with newdiagnostic weights and scores, and function 170 now reviews thecandidate diseases to see if another iteration of the diagnostic loop isin order, or if diagnostic session goals or limits have been reached.Function 170 begins with entry state 2202, where all candidate diseaseshave just been updated. Moving to a decision state 2204, function 170reviews the diagnostic momentum and score of each candidate disease. Ifany one disease has advanced significantly enough to be selected as thenext current disease, function 170 moves to state 2208 to set thediagnostic mode to VAI for that disease, and then function 170 moves tofunction 2210. But if, at decision state 2204, no one disease has madespecial progress toward a diagnosis, function 170 moves to state 2206 toset the diagnostic mode to HAI, and then moves to function 2210.Function 2210 reviews the diagnostic goals and limits, which is furtherdescribed in conjunction with FIG. 23. At the completion of function2210, function 170 advances to state 2212 and checks to see if anyprocess in the loop has requested termination, adjournment or otherinterruption, or has modified the diagnostic mode or parameters oroptions used in the diagnostic loop 100. State 2212 also processesactions requests triggered by the action plateau feature, which allowsany disease object to request premature termination of the loop in favorof performing some other action such as is sometimes required inemergency cases. Moving to state 2214, function 170 sets internal flagsto either continue or terminate the diagnostic loop. Moving to state2216, function 170 returns to the caller, which in this case is state172 of FIG. 1.

Referring now to FIG. 23, the Check Goals and Limits function 2210, asused in the diagnostic loop (FIG. 22), will be described. In function2210, all candidate disease scores and diagnostic rankings have justbeen updated, and the function must now review the candidate diseases tosee if global diagnostic session goals or limits have been reached. Thediagnostic system is poised for another iteration of the diagnostic loop100, and in function 2210 it determines whether another iteration is infact required or advisable.

Function 2210 begins with entry state 2302, where all candidate diseaseshave just been updated. Moving to a decision state 2304, if there are nomore candidate diseases to be diagnosed, function 2210 moves to state2324 to set the loop termination flag. But if, at decision state 2304,there are more candidate diseases, function 2210 moves to a decisionstate 2306. At decision state 2306, if the diagnostic goal is to rulesome given number n of diseases in (or out), function 2210 moves to adecision state 2308; otherwise it moves to a decision state 2310. Atdecision state 2308, if at least n diseases have indeed been ruled in(or out), function 2210 moves to state 2324 to set the loop terminationflag; otherwise function 2210 moves to decision state 2310. At decisionstate 2310, if the diagnostic goal is to rule certain specified diseasesin (or out), function 2210 moves to a decision state 2312; otherwise itmoves to a decision state 2314. At decision state 2312, if the specifieddiseases have indeed been ruled in (or out), function 2210 moves tostate 2324 to set the loop termination flag; otherwise function 2210moves to decision state 2314.

At decision state 2314, if the diagnostic loop is limited to some giventime interval, function 2210 moves to a decision state 2316; otherwiseit moves to a decision state 2318. At decision state 2316, if thespecified time interval has elapsed, function 2210 moves to state 2324to set the loop termination flag; otherwise function 2210 moves todecision state 2318. At decision state 2318, if the diagnostic loop islimited to some given number of questions, function 2210 moves to adecision state 2320; otherwise it moves to state 2322. At decision state2320, if the specified number of questions have been asked, function2210 moves to state 2324 to set the loop termination flag; otherwisefunction 2210 moves to state 2322. At state 2322, function 2210 sets aninternal flag to continue the diagnostic loop and moves to return state2326. At state 2324, function 2210 sets an internal flag to terminatethe diagnostic loop and moves to state 2326 and returns to function 170(FIG. 22).

Referring now to FIG. 24, the Shut Down Diagnostic Loop function 180(FIG. 1) of the diagnostic loop 100 will be described. At the entry tofunction 180, the diagnostic loop is being terminated, and function 180now performs the actions that are part of an orderly termination andshut-down of the loop.

Function 180 begins with entry state 2402. Moving to state 2404,function 180 generates the required diagnostic reports. Moving to state2406, function 180 saves newly generated disease and symptom data.Moving to state 2408, function 180 saves the diagnostic loop “state” attermination, which enables the system to continue the loop at a futuretime by resetting all variables to the same “state”. Moving to state2410, function 180 releases all computer and system resources that wereallocated to the diagnostic loop 100. Moving to state 2412, function 180returns to state 182 of FIG. 1.

Referring now to FIG. 25, the Slope function 2500 that computes theslope of two values with respect to time will be described. The slope ofan angle is its tangent, and the time slope of two values v1 and v2 is(v2−v1)/(t2−t1). When t1=t2, this value is arithmetically undefined, andwhen t1 approaches t2, it will raise overflow conditions in digitalcomputers. This function uses an auxiliary result flag that is suitablycoded to inform the caller about any special problems encountered duringthe calculation.

Function 2500 begins with entry state 2502, where the arguments t1, t2,v1, and v2 are assumed to be available to the function. Moving to state2504, function 2500 examines data value v1. Proceeding to a decisionstate 2506, if no value v1 is given, function 2500 moves to state 2508.At state 2508, function 2500 sets the result flag to indicate “no valuev1” and returns at return state 2526. But if, at decision state 2506,there is a value v1, function 2600 moves to state 2510 and retrievesdata value v2. Moving to a decision state 2512, if no value v2 is given,function 2500 moves to state 2514, sets the result flag to indicate “novalue v2” and then returns at state 2526. But if, at decision state2512, there is a value v2, function 2500 moves to state 2516 andcomputes the tangent (v2−v1)/(t2−t1). Continuing to a decision state2518, if the result of state 2516 raised an overflow error condition,function 2500 moves to state 2520, sets the result flag to indicate“infinite slope” and returns at state 2526. But if, at decision state2518, the result did not overflow, function 2500 moves to state 2522 andsets the result slope to the slope computed in state 2516. Moving tostate 2524, function 2500 sets the result flag to indicate “normalslope”. Moving to state 2526, function 2500 returns the result slope andflag to the function (2600, FIG. 26 or 1510, FIG. 16) that called it.

Referring now to FIG. 26, the Trend function 2600, which computes thetrend of three points with respect to time, will be described. Ascomputed here, the time trend has three possible values: DECREASING,CONSTANT, and INCREASING, depending on whether the slope from point 2 topoint 3 is less than, equal to, or greater than the slope from point 1to point 2, respectively. For various values of t1, t2, and t3,computing these slopes may be arithmetically undefined or raise overflowconditions in digital computers; the function uses an auxiliary resultflag that is suitably coded to inform the caller about such specialconditions encountered during the calculation.

The Trend function 2600 begins with entry state 2602, where thearguments t1, t2, t3, v1, v2, and v3 are assumed to be available to thefunction. Moving to state 2604, function 2600 examines data value v1.Advancing to a decision state 2606, if no value v1 is given, function2600 moves to state 2608, sets the result flag to indicate “no value v1”and returns at return state 2644. But if, at decision state 2606, thereis a value v1, function 2600 moves to state 2610 and retrieves datavalue v2. Moving to a decision state 2612, if no value v2 is given,function 2600 moves to state 2614, sets the result flag to indicate “novalue v2” and returns at state 2644. But if, at decision state 2612,there is a value v2, function 2600 moves to state 2616 and retrievesdata value v3. Moving to a decision state 2618, if no value v3 is given,function 2600 moves to state 2620, sets the result flag to indicate “novalue v3” and returns at state 2644. But if, at decision state 2618,there is a value v3, function 2600 moves to execute function 2500 (FIG.25).

At function 2500, the slope from point 1 to point 2 is computed as(v2−v1)/(t2−t1). Moving to a decision state 2624, if the result ofexecuting function 2500 raised an overflow error condition, function2600 moves to state 2626. At state 2626, function 2600 sets the resultflag to indicate “infinite slope 1” and returns at state 2644. But if,at decision state 2624, the result did not overflow, function 2600 movesto execute function 2500′ (FIG. 25). At function 2500′, the slope frompoint 2 to point 3 is computed as (v3−v2)/(t3−t2). Moving to a decisionstate 2630, if the result of function 2500′ raised an overflow errorcondition, function 2600 moves to state 2632. At state 2632, function2600 sets the result flag to indicate “infinite slope 2” and returns atstate 2644. But if, at decision state 2630, the result did not overflow,function 2600 moves to a decision state 2634 and compares slope 1 toslope 2, using predefined comparison ranges. If, at decision state 2634,slope 1 is greater than slope 2, function 2600 moves to state 2636; ifslope 1 is less than slope 2, function 2600 moves to state 2640; ifslope 1 is equal to slope 2, function 2600 moves to state 2638. At state2636, function 2600 sets the result trend to indicate a decreasingtrend, and then moves to state 2642. At state 2638, function 2600 setsthe result trend to indicate a constant trend, and then moves to state2642. At state 2640, function 2600 sets the result trend to indicate anincreasing trend, and then moves to state 2642. At state 2642, function2600 sets the result flag to indicate a normal result. Moving to state2644, function 2600 returns the computed trend and the result flag tothe calling function 1520 (FIG. 17).

Referring to FIG. 27, a simple conceptual way of showing the use ofactual and alternative symptom weights in arriving at a diagnostic scorewill now be described. Although the actual implementation may differ,the diagram illustrates the relationships among the diseases, symptomsand weights. In one embodiment, a disease-symptom matrix is used where aplurality of diseases are listed in the columns and a plurality ofrelated symptoms are listed in the rows. In a partial exemplary headachedisease-symptom matrix 2700, the column 2702 lists symptoms in the rowsmarked as 2732 and at 2742. Several exemplary diseases are shown at thecolumns marked as 2704 (common migraine), 2706 (classic migraine), 2708(cluster headache), and 2710 (subarachnoid hemorrhage). For eachdisease, there is a column for holding an “actual” value (columns 2714,2716, 2718, and 2720) or an “alternative” value (columns 2715, 2717,2719, 2721). As described above, a consultation begins in HAI mode inthe area marked 2730. Questions to elicit exemplary symptoms in the areamarked 2732 are asked of the patient. As questions associated with thesymptoms are asked of the patient, a value for the particular symptom isplaced in either the actual column or the alternative column for eachapplicable disease as assigned by the author of a particular disease.For example, a symptom “Nausea2” is defined by the author of CommonMigraine to be an “actual” symptom and has a value of 35 as determinedby the answer of the patient. However, the symptom “Nausea2” is definedby the author of Cluster Headache to be an “alternative” symptom and hasa value of −20 as determined by the answer of the patient. Therefore,while in HAI mode, some diseases will have actual symptoms elicited andsome diseases will have alternative symptoms elicited.

After one of several possible criteria is reached, the mode is switchedfrom HAI mode (2730) to VAI mode (2740) by the system, such as shown at2734. From that point forward, the system concentrates on asking thesymptoms for a focus disease based on reaching the criteria. In oneembodiment, the “actual” symptoms for the focus disease (classicmigraine in this example) are then elicited as shown at 2742. Forexample, the criteria may include the fact that a particular diagnosticscore is reached or passed, a particular diagnostic momentum isachieved, a probability of diagnosis is achieved, or the user may ask toswitch modes. The user may request to have the actual symptoms for thefocus disease to be elicited, or even have the system go back and re-askonly the actual symptoms for the focus disease as shown at 2744. Otherweights (not shown), such as for various types of synergies, are addedin for the diseases in the area marked 2746. The exemplary diagnosticscores for each disease column are shown at row 2750 and a total scorefor each disease, which sums the actual and alternative scores for eachdisease, is shown in row 2752. A diagnosis may be declared for a diseasehaving a total score that meets or exceeds a particular threshold. Inthis example, the system diagnoses the patient as having classicmigraine, with a score of 480.

Referring now to FIG. 28, this figure depicts a form or screen displaythat lets a patient arrange a set of symptoms into the time order inwhich they actually occurred in the patient. This is one embodiment thatuses a graphic user interface and input form to obtain the patient'sinput. Other embodiments use other techniques to obtain the sameinformation from the patient. In the diagnostic loop 100 (FIG. 1), thesystem uses a valuator object (FIG. 6) to obtain the patient's diseasetimeline as a value. The valuator object then builds a time profile ofthe patient's symptoms, compares it to time profiles previously storedin a database, and adds extra diagnostic weight to diseases that matchthe patient (FIG. 20). In another part of the diagnostic loop, thepatient's disease timeline profile can be used, by well-known patternmatching techniques, to filter the candidate disease list and thusreduce it to the most likely candidate diseases (FIG. 3). In anotherpart of the diagnostic loop, the patient's disease timeline can be usedto identify the First Significant Symptom (FIG. 13). In another part ofthe diagnostic loop, the patient's disease timeline can be used toselect a disease that closely matches the patient time profile as thenext focus disease (FIG. 4). Disease timelines, therefore, can be usedto both reduce the set of candidate diseases as well as to help diagnosea particular candidate disease.

FIG. 28 shows a chart 2800 that plots symptom onset and duration againsttime for four different symptoms. The time scale (line 2810) is shown inhours and runs from left to right, so that symptoms that appear earlierare placed more to the left than symptoms that occur later. As anexample, four symptoms (Anorexia, Nausea, Epigastric Pain, andRight-Lower-Quadrant Pain) are shown to depict the chronologicalsequence in which they typically appear in the disease Appendicitis.Other diseases may, of course, also exhibit this same time line or timeprofile of symptoms. The chart shows (line 2812) that classicAppendicitis typically begins with Anorexia (loss of appetite), which istherefore placed at the very left of the time line, to mark the originor start of the disease process in the patient. One hour after the onsetof Anorexia, a patient with Appendicitis will typically experienceNausea, which is therefore shown as starting at the 1-hour mark (line2814). About 2.5 hours into the disease, a patient will typicallyexperience Epigastric Pain (discomfort in the stomach area), and so thatsymptom is shown (line 2816) as starting between time 2 and 3 on thetime scale. Similarly, RLQ Pain (pain in the right lower quarter of theabdomen) is shown as beginning about 4 hours after the disease starts(line 2818). Assuming that the patient has previously indicated thepresence of the symptoms Anorexia, Nausea, Epigastric Pain, and RLQPain, the diagnostic system offers this type of chart to the patient.The patient then uses a mouse to move the symptom blocks left and rightuntil they are in a position that indicates when they appeared. Theblocks themselves can be stretched or shrunk to indicate how long theylasted. Then the patient submits the selections by clicking on a Submit(or similar) button 2820.

Referring now to FIG. 29a, an object embodiment 2900 of the entire MDATAsystem using techniques of object-oriented programming, i.e., as acollection of software objects that are capable of diagnosing a patient.In this embodiment 2900, all data is converted into “objects” bysurrounding the data (now called “members”) with software procedures andfunctions (now called “methods”) that manipulate and maintain the data.Programs outside of the object are only permitted to access member databy using an object method or by special permission. This object-orientedembodiment represents a rearrangement and redistribution of data andprocesses that has several benefits. First, the object-orientedembodiment guarantees the validity of the object's data, data formats,and data structures at all times, regardless of the unpredictabledynamics of the surrounding diagnostic environment. Second, each objectof a system can accumulate data, thus tracking both its own processinghistory and that of its neighboring objects. It can therefore assess itscurrent status, compare itself to other objects, and acquire anawareness that it can use to make intelligent decisions. Third, given amemory, an awareness, and methods for acting on its environment permitsan object to be used as an agent that can act independently to perform atask which is useful to the system as a whole.

The object-oriented embodiment is well known in the programmingindustry, but is used here in a novel and non-obvious manner to performautomated medical diagnosis. In other diagnostic systems, diseases andobjects are treated as inanimate data records that are manipulated by acentral program to compute a diagnosis. In the embodiment described inFIGS. 29a and 29 b, diseases and symptoms are designed as objects thatcan behave as intelligent actors that organize themselves at variouslogical levels, cooperate competitively for diagnosis, and ultimatelysort themselves into a differential diagnosis. For the purpose ofillustration, a software object is represented as a simple circle thatis assumed to encapsulate all of the object's data, and severalrectangles that represent object functions that belong to the object butcan be accessed by the outside world. Seen as such, a software objectcan be used as a “smart data record” that can act independently of otherobjects and can retain a memory of its actions for future reference.

FIG. 29a shows several such objects, as they would be designed toparticipate in the object embodiment 2900 of the MDATA system. Object2901 may represent a portion of the diagnostic system, e.g., thelist-based engine, whose actions are detailed in FIGS. 1 to 26. Thefunctions performed by the system in the object embodiment are showngrouped around the object. For example, the function INITIALIZE SELFwould be called by the external system to cause the system to preparefor a diagnostic session, and the function REPORT STATUS would be calledby any process requiring some information about the current status ofthe system. Object 2902 represents the Candidate Disease List; itsfunctions perform services related to that list. For example, thefunction FORM CANDIDATE DISEASE LIST would be called by the systemobject to begin a diagnostic session as shown in FIG. 2. Object 2903 isa Disease Object, which represents all of the medical knowledge thesystem has about a single disease. Its functions provide servicesrelated to diseases, such as performing a diagnosis or updating itselfwith a new diagnostic score. Object 2904 is a Symptom Object, whichrepresents the data and functions related to a single symptom. Forexample, one of its functions is to GET VALUE AT TIME T, which activatesthe appropriate valuator functions needed to obtain a value, bycomputation, table lookup, or questioning of the patient. Object 2905 isa Valuator Object, whose role is to perform the detailed actionsrequired to obtain a value. Object 2906 is a Question Object, whichhandles the tasks required to question a human patient. Object 2907 is aNode Object, whose role is to handle the actual interface between thedigital computer and the human patient. In one embodiment, the nodeobject is the only object in the entire MDATA system that actuallycommunicates with the patient.

Referring now to FIG. 29b, an object-oriented embodiment of how theMDATA system (FIG. 29a) might use a collection of objects to perform asingle iteration of the Diagnostic Loop (FIG. 1) will be abstractlydescribed. Instead of using a single program or engine that contains aset of central functions that perform operations on data (FIG. 1), theobject-oriented embodiment of FIG. 29b uses functions distributed as‘methods’ into various objects that either perform the functionsthemselves or delegate operations to still other objects. For example,whereas in FIG. 1, the system calls a function to select the next focussymptom, in FIG. 29b, it is the current disease object that selects thenext symptom.

In general, each object performs its own tasks and calls upon otherobjects to perform their tasks at the appropriate time. Over time, thesequence of operations creates a natural task hierarchy, from higher tolower levels of detail, using as many or as few levels as is required toperform the main task. At the same time, the hierarchy representslogical interpretations and meanings at different levels. Thus, at thelowest level, a patient is answering a single question; at the middlelevel, this is interpreted as a change in symptom information about thepatient; at the highest level, it may result in a reranking of thetentative diagnosis of several competing diseases.

The ability to capture and embody the complex interpretive andanalytical tasks of medical diagnosis into software is what gives theMDATA design a major advantage over other automated diagnostic systems,which tend to operate at a single level of action and meaning. What isgained by the object embodiment is the self-organizing andself-assessment ability that emerges from a system whose data areallowed to have the autonomy to organize themselves, based on certainglobally controlling principles.

FIG. 29b summarizes a process 2915 that may take place in anobject-oriented embodiment of the MDATA system in order to perform thesame processes that are detailed in FIGS. 1 through 26, with the addedcomplication that questions are posed to an on-line patient in thepatient's native language or other desired language, for example,French. The process 2915 starts when an external process has assembled aset of candidate diseases and now calls method 2921 to compute adifferential diagnosis based on the candidate diseases. Note that thefurther processing by the system, occurs to output a particulardiagnosis. Method 2921 is one of many method functions of Engine Object2920. Engine Object 2920 encapsulates all data and processes ofprocesses of at least a portion of the system and its numerous methods,of which only methods 2921 and 2922 are shown here for illustration. Themain purpose of the Engine Object is to accept external system and userrequests and initiate the appropriate internal processing. At theappropriate time, process 2915 advances to method 2931, which is amethod of a Disease Object 2930. Disease Object 2930 encapsulates alldata and processes of a typical Disease Object. It has numerous methods;only methods 2931 and 2932 are shown here for illustration. A DiseaseObject represents all that is known about one given medical disease; itsmain function is to accept requests for disease information and to carryout external requests for action. In the case being illustrated, theDisease Object 2930 selects one of its Symptom Objects and moves tomethod 2941 to obtain the value of that symptom in the on-line patient.Symptom Object 2940 contains all data and processes of a typical SymptomObject, with many methods of which only methods 2941 and 2942 are shownhere for illustration. A Symptom Object represents all that is knownabout one given symptom; its main function is to accept requests forsymptom information. The Symptom Object initiates internal processing toobtain actual symptom values and proceeds to method 2951.

Method 2951 is one method of a Question Valuator Object 2950. ValuatorObject 2950 has numerous methods, but only methods 2951 and 2952 areshown here. In general, a Valuator Object is responsible for performingthe computations required to compute the value of a symptom at some timet. A question valuator object initializes the processing required toselect and ask a human questions and then advances to method 2961.Method 2961 is a method of Question Object 2960, which represents themembers and methods of a typical Question Object and its many methods,of which only methods 2961 and 2962 are shown here. A Question Objectrepresents all of the data involved in asking a human a question, suchas the natural language to be used or the education level of thepatient. The object's main function is to handle the stream of detailedquestions that are usually required to pose a question and elicit avalid response from a human. This question object selects the nodeobjects that are written to display questions in the patient's nativelanguage. Process 2915 then moves to method 2971 of Node Object 2970.Node Object 2970 encapsulates all data and processes of a typical nodeobject and its methods (only methods 2971 and 2972 are shown here). Thisnode object handles the physical details of a single question/responseexchange between a human and a computer, including special hardware,video, and audio problems, time delays, time-outs, and any need torepeat. The Node Object initiates the requisite processing, and thenmoves to method 2981. Method 2981 asks a question of a patient who maybe accessing the system over a data communications network, theInternet, for example. Assuming a GUI embodiment, the question will bedisplayed on a screen as a dialog, with appropriate buttons for aresponse from the patient via a patient object 2980.

When the patient responds at state 2982, a reverse sequence of methodsbegins as the process 2915 backs up the object hierarchy. From state2982, process 2915 moves to method 2972, where the patient's response isnoted and time-stamped. Proceeding to method 2962, the response is notedas the final response in a possible question stream. Moving to method2952, the response is encoded for internal processing, so that it losesits native language aspect here. Moving to method 2942, the response isprocessed as a new symptom value at time t. Moving to method 2932, theresponse is processed as an increment in the disease's diagnostic score.Moving to method 2922, the response activates a reranking of therelative disease positions in a differential diagnosis and then performsthresholding to determine a diagnosis. Finally, the engine object 2920can now repeat the process or terminate, depending on the externaloption settings. Other functions and objects associated with the LBEwhich are expected before, during or after this control flow may beincluded in various embodiments. For instance, once a disease ranking iscomputed, thresholding may occur to determine a diagnosis of onedisease. Furthermore, actions may occur such as updating a patient'selectronic medical record or notifying a doctor or other healthcarepractitioner of a diagnostic event, e.g., a situation requiringimmediate treatment.

Referring now to FIG. 30, a process 3000 describes how alternativesymptoms are established in an off-line preparation mode and later usedto diagnose a patient in an on-line mode. The off-line and on-line modesare shown here as if they occur in sequence; but in practice they aretypically separated by additional preparation steps such as testing,approval, auditing, and final integration into a production database.

Process 3000 begins at state 3002, where one or more disease objects areto be created by a medical author. Moving to state 3004, one diseaseobject D is established by defining its member functions and data. Onemajor data structure of disease D is the list of symptoms thatcharacterize the disease. Each symptom S of that list must be definedand described. Moving to state 3006, one symptom S of the list isestablished and defined in terms of its values and their diagnosticweights. Moving to a decision state 3010, if the symptom object canperhaps be used as alternative symptoms in other disease objects, thediagram moves to state 3012; otherwise it moves to a decision state3014. At state 3012, the symptom is established as an alternativesymptom in the applicable disease objects. Moving to decision state3014, if there are more symptom objects to be established for disease D,the diagram moves back to state 3006; otherwise it moves to a decisionstate 3016. At decision state 3016, if there are more disease objects tobe established, the diagram moves back to state 3004; otherwise itterminates the off-line phase.

For the on-line phase, process 3000 describes how alternative symptomsare processed inside the diagnostic loop 100 during an automateddiagnostic session with a patient (or a patient's agent) who is on-lineand able to respond to questions posed by the system (FIG. 1). Moving tostate 3030, a disease D is selected as the focus for diagnosis (FIG. 4).Moving to state 3032, one symptom object S of disease D is selected asthe focus for diagnosis (FIG. 5). Symptom S must now be evaluated, whichmay be a complex, time-consuming process (FIG. 6). Moving to a decisionstate 3034, process 3000 shows a portion of the evaluation process thatdeals with the use of alternative symptoms, which are used to saveon-line time. It may be the case that, for disease D, symptom S has anacceptable alternative symptom that has already been evaluated. If so,the diagram bypasses evaluation of symptom S and instead moves to state3036. But if, at decision state 3034, there is no alternative symptom,process 3000 moves to state 3038 for evaluation of symptom S. At state3036, the weights of the alternative to symptom S are retrieved andapplied to the diagnostic score of disease D, and then process 3000moves to a decision state 3040. However, at state 3038, symptom S isevaluated (FIG. 6), the diagnostic weights of symptom S are retrievedand applied to the diagnostic score of disease D (FIGS. 11 and 21), andthen process 3000 moves to state 3040. At state 3040, if someterminating condition is reached for disease D (FIG. 22), process 3000moves to a decision state 3044; else it moves to state 3032. At decisionstate 3044, if there are other disease objects to process, process 3000moves back to state 3030; otherwise it moves to the end state 3046.

Referring now to FIG. 31, a process 3100 describes how symptom objectscan be re-used as alternative symptoms in a disease object. TheAlternative Symptoms feature allows the diagnostic system to substitutespecified symptom values for others in order to bypass time-consumingevaluation of a given symptom when acceptable alternative symptom valuesare already available. The feature accommodates the individualpreferences of medical authors, simplifies the processing of symptomsstored in various equivalent formats, and allows the sequencing ofsymptom evaluation to be more adaptive to the dynamics of an on-linediagnostic session, instead of depending on prescribed sequence ofsymptom evaluation.

Process 3100 only shows the general steps of the off-line preparation ofone disease object. How the disease is diagnosed on-line is shown inFIG. 1, and how alternative symptoms are used is described inconjunction with FIGS. 6 and 30. Process 3100 begins at state 3102,where an author wants to create and describe a disease object D. Movingto state 3104, the disease object D is established and its memberfunctions and data are defined. One major data structure of disease D isthe list of symptoms, symptom values, and symptom timelines thatcharacterize the disease. Each symptom of that list is identified anddescribed. Moving to state 3110, a database of all existing symptomobjects is accessed for the purpose of locating possible alternativesymptoms. Advancing to state 3112, one symptom S of disease D's symptomlist is selected. Proceeding to state 3114, the symptom database issearched, and some symptom A is identified as an acceptable alternativeto symptom S for diagnosing the disease D. Moving to state 3116,diagnostic weights are assigned to the values of alternative symptom A.Moving to a decision state 3118, if more symptoms of disease D are to beprocessed, process 3100 moves back to state 3112; otherwise it moves toend state 3122.

Referring now to FIG. 32a, a process 3200 used by an author to establishdisease and symptom objects or elements will be described. Note that thediagram shows processing that is performed off-line, at data preparationand testing time, and not on-line at diagnostic time.

Process 3200 begins at state 3202, where the author wants to define oneor more disease objects and their symptom objects. Moving to state 3204,multiple disease objects are established, each with the requisitedisease object data and disease object processing functions. Proceedingto state 3206, multiple symptom objects are identified for each diseaseobject. Symptoms that already exist in a symptom database areidentified. New symptoms are described, including a list of theirpossible values. Advancing to state 3208, a diagnostic weight isassigned to some or all values of each symptom object. Each symptomobject typically has many possible values, and each value may have anassigned diagnostic weight toward the diagnosis of its associateddisease. Moving to state 3212, process 3200 ends the off-line portion atstate 3212.

Referring to FIG. 32b, one embodiment of the use of specified symptomelements or objects for disease elements or objects will now bedescribed by on-line process 3230. The off-line portion (process 3200)was previously described in conjunction with FIG. 32a. Process 3230illustrates one embodiment of the HAI and VAI concepts along with theuse of specified symptom elements.

Process 3230 begins at an enter state 3232 in the HAI mode previouslydescribed. Process 3230 advances to state 3234 where a selected symptomelement is evaluated, such as performed by the Obtain Symptom Valuefunction 140 (FIG. 6). Proceeding to state 3236, process 3230 appliesthe diagnostic weight of the symptom value to the diagnostic score ofdisease elements for which the symptom element is applicable. Moving toa decision state 3238, process 3230 determines if the criteria forswitching to the evaluation of specified symptom elements (i.e., in theVAI mode) for one of the disease elements (the Nth disease element) isreached. Examples of criteria are a high diagnostic momentum, a highdiagnostic score, a high diagnostic probability, some external processrequested the switch or a human requested the switch to the use ofspecified symptoms. If the criteria is not reached at decision state3238, process 3230 proceeds to state 3240 to continue evaluation ofsymptom elements where the specified symptom elements for any diseaseelement are not necessarily selected. Process 3230 moves back to state3234 to loop through further symptom elements until the criteria atdecision state 3238 is reached.

If the criteria is reached at decision state 3238, process 3230 proceedsin the VAI mode to state 3242 where a specified symptom element for theNth disease element is evaluated. Continuing at state 3244, process 3230applies the weight of the evaluated symptom element to the diagnosticscore of the Nth disease (and any other disease for which it is aspecified symptom element) and alternative symptom weight(s) to thediagnostic score(s) of other appropriate disease element(s). Advancingto a decision state 3246, process 3230 determines if the diagnosticscore for any disease element has reached or passed a threshold fordiagnosis. If so, the diagnostic scores for the relevant diseases (thosebeing currently evaluated) are returned at end state 3248. If thediagnostic score for any disease element has not reached or passed thethreshold, as determined at decision state 3246, process 3230 proceedsto a decision state 3250 to determine if further specified symptomelements are available to be evaluated for the Nth disease element. Ifso, process 3230 continues at state 3252 to continue evaluation ofsymptom elements where the specified symptom elements for the Nthdisease element are selected. Process 3230 moves back to state 3242 toloop through specified symptom elements of the Nth disease until thereare no further specified symptom elements available, as determined atdecision state 3250. When decision state 3250 determines there are nofurther specified symptom elements available, process 3230 advances tostate 3254 to check the diagnostic score for each of the remainingdisease that are currently being evaluated. Process 3230 then moves backto decision state 3238 to determine if the VAI mode should beestablished any other disease element or if further processing shouldcontinue in HAI mode, as previously described.

Referring now to FIG. 33, a process 3300 summarizes the major steps of afully automated method that uses the novel concept of disease timelinematching to diagnose a patient's medical condition. A disease timelineis a data structure that records the symptoms of a specific disease inchronological order and describes the key symptom characteristics suchas onset, duration, magnitude, and offset of each symptom. Diseasetimelines can be generic or actual. Generic timelines describe thedisease statistically, as it evolves in a typical population; actualtimelines describe the symptoms of a specific patient being diagnosed.Timelines are abstract data structures that can be represented invarious ways such as graphs, charts, calendars, lists, tables,spreadsheets, or software objects. A simple example is an hour-by-hourdescription of a disease process in the form of a Gantt Chart, as shownin FIG. 28.

The diagnostic process 3300 outlined in FIG. 33 exploits the fact thatit is possible to take an actual disease timeline, use well-knownmathematical techniques to match it to a database of generic diseasetimelines, and thus identify a relatively small set of diseases that arestrong candidates for further analysis and ultimate diagnostic ranking.As outlined in FIG. 33, the process has two separate phases. In thefirst phase (states 3302 to 3304), a computer program assists physicianauthors as they prepare a database of generic disease timelines. In thesecond phase (states 3306 to 3320), another program uses various symptomcharacteristics to match the timeline of an on-line patient to thegeneric timelines in the database and to appropriately increment thediagnostic score of matching diseases.

State 3302 is the start of the process 3300. Moving to state 3304, usingan off-line program, medical authors generate a database of genericdisease timelines. This state may represent several years ofprofessional labor to generate a massive database of thousands ofdiseases and their symptoms. It may also include further extensive laborto format and test the data and to prepare it for use in a fullyautomated diagnostic system such as described in states 3306 through3320.

Proceeding to state 3306, a patient is on-line to a computer programwhich elicits a chief complaint from a patient by asking questions.Moving to state 3308, the program receives answers from the patient.Advancing to state 3310, the program uses the responses to identify oneor more diseases that correspond to the chief complaint. Moving to state3312, the program correlates the chief complaint to a timeline for allidentified diseases.

Continuing at state 3314, the program asks the patient questions todetermine the patient's FSS time parameters and locates this on thedisease timeline. Moving to state 3316, the program adds apre-determined incremental diagnostic weight to the diagnostic score ofall identified diseases if the patient's FSS matches the disease FSS.Proceeding to state 3318, the program establishes a diagnosis for one ormore of the identified diseases when its cumulative diagnostic scorereaches or exceeds a threshold. The process 3300 ends at state 3320.

Referring now to FIG. 34, a fully automated process 3400 that diagnosesa patient's medical condition using symptom magnitude patterns, whichidentify the changes in magnitudes of each symptoms of a specificdisease in chronological order, will be described. A magnitude patternis, in effect, a disease timeline with magnitudes of the symptoms,creating a disease profile. Like a disease timeline, a symptom magnitudepattern can be can be generic or actual, i.e., typical orpatient-specific.

Process 3400 begins at state 3402. Moving to state 3404, using anoff-line program, medical authors generate a database of generic symptommagnitude patterns, analogous to the disease timelines described forFIG. 33. Proceeding to state 3406, a computer program asks an on-linepatient questions to elicit the magnitude of the patient's symptom.

Continuing at state 3408, the program develops a pattern or profile ofthe patient's symptoms and their magnitudes. Moving to state 3410, theprogram compares the patient's symptom magnitude pattern to its databaseof symptom magnitude patterns, to (attempt to) identify the patient'sdisease. Process 3400 ends at state 3412.

Referring to FIG. 35, a block diagram of one embodiment of the MDATAsystem 3500 will be described. The MDATA system 3500 includes a networkcloud 3502, which may represent a local area network (LAN), a wide areanetwork (WAN), the Internet, or another network capable of handling datacommunication.

In one embodiment, the MDATA programs and databases may reside on agroup of servers 3508 that may be interconnected by a LAN 3506 and agateway 3504 to the network 3502. Alternatively, in another embodiment,the MDATA programs and databases reside on a single server 3510 thatutilizes network interface hardware and software 3512. The MDATA servers3508/3510 store the disease/symptom/question lists or objects describedabove.

The network 3502 may connect to a user computer 3516, for example, byuse of a modem or by use of a network interface card. A user 3514 atcomputer 3516 may utilize a browser 3520 to remotely access the MDATAprograms using a keyboard and/or pointing device and a visual display,such as monitor 3518. Alternatively, the browser 3520 is not utilizedwhen the MDATA programs are executed in a local mode on computer 3516. Avideo camera 3522 may be optionally connected to the computer 3516 toprovide visual input, such as visual symptoms.

Various other devices may be used to communicate with the MDATA servers3508/3510. If the servers are equipped with voice recognition or DTMFhardware, the user can communicate with the MDATA program by use of atelephone 3524. A telecommunications embodiment, e.g., using atelephone, is described in Applicant's U.S. patent entitled“Computerized Medical Diagnostic and Treatment Advice System,” U.S. Pat.No. 5,660,176, which is hereby incorporated by reference. Otherconnection devices for communicating with the MDATA servers 3508/3510include a portable personal computer 3526 with a modem or wirelessconnection interface, a cable interface device 3528 connected to avisual display 3530, or a satellite dish 3532 connected to a satellitereceiver 3534 and a television 3536. Other ways of allowingcommunication between the user 3514 and the automated diagnostic system3508/3510 are envisioned.

VI. CONCLUSION

Specific blocks, sections, devices, functions and modules may have beenset forth. However, a skilled technologist will realize that there aremany ways to partition the system of the present invention, and thatthere are many parts, components, modules or functions that may besubstituted for those listed above.

As should be appreciated by a skilled technologist, the processes thatare undergone by the by the above described software may be arbitrarilyredistributed to other modules, or combined together in a single module,or made available in a shareable dynamic link library. The software maybe written in any programming language such as C, C++, BASIC, Pascal,Java, and FORTRAN and executed under a well-known operating system, suchas variants of Windows, Macintosh, Unix, Linux, VxWorks, or otheroperating system. C, C++, BASIC, Pascal, Java, and FORTRAN are industrystandard programming languages for which many commercial compilers canbe used to create executable code.

While the above detailed description has shown, described, and pointedout the fundamental novel features of the invention as applied tovarious embodiments, it will be understood that various omissions andsubstitutions and changes in the form and details of the systemillustrated may be made by those skilled in the art, without departingfrom the intent of the invention.

What is claimed is:
 1. A computerized medical diagnosis system,comprising: a) means for defining a spectrum of terms representative ofa subjective description for an aspect of a medical symptom; b) meansfor presenting the spectrum of terms to a patient during a diagnosissession; c) means for selecting a term from among the spectrum of terms;d) means for repeating a)-c) for other aspects of the medical symptom;e) means for encoding the selected terms; and f) means for indexing adatabase of diseases with the encoded terms thereby diagnosing adisease.
 2. A computerized medical diagnosis system, comprising: a)means for defining a spectrum of terms representative of a subjectivedescription for a selected aspect of a medical symptom; b) means fordefining diagnostic weights for each term of the spectrum; c) means forpresenting the spectrum of terms to a patient during a diagnosticconsultation; d) means for receiving a selected term from among thespectrum of terms; e) means for corresponding the selected term to aweight; f) means for applying the weight corresponding to the selectedterm to a diagnostic score so as to diagnose a medical condition; g)means for repeating the acts a)-f) for other aspects of the medicalsymptom so as to receive other selected terms; and h) means for encodingthe selected terms into a code.
 3. The system defined in claim 2,additionally comprising: means for repeating the acts c)-h) at apredetermined later time; means for analyzing a change of the code overtime; and means for assigning a weight for the change of the code forthe medical symptom over time.
 4. The system defined in claim 3, whereinthe predetermined later time is later in the diagnostic consultation. 5.The system defined in claim 3, wherein the predetermined later time isduring a subsequent diagnostic consultation.
 6. The system defined inclaim 3, additionally comprising means for storing the code over time ina patient medical history.
 7. The system defined in claim 6,additionally comprising means for retrieving past codes from the patientmedical history.
 8. The system defined in claim 3, wherein the means foranalyzing the change of the code over time includes means for computinga synergy based on the codes.
 9. The system defined in claim 8, whereinthe means for computing a synergy based on the codes includes means fordetermining a slope, trend, area or volume of a plot of the codes overtime.
 10. The system defined in claim 2, wherein the aspects areselected from a group comprising quality, severity, location, size,symmetry, timing, localizability, and migration.
 11. The system definedin claim 10, wherein the quality aspect includes the terms: pinprick,knifing, tearing, fullness, tightness, and pressure.
 12. The systemdefined in claim 10, wherein the severity aspect includes terms defininga magnitude range.
 13. A method of developing a data structure ofaspects of a medical symptom for use in diagnosis of medical conditions,the method comprising: identifying a plurality of aspects of aparticular medical symptom; defining a spectrum of terms for each aspectof the plurality of aspects; assigning a corresponding diagnostic weightfor each term of each spectrum of terms; and storing the aspects, thespectrum of terms for each aspect and the corresponding diagnosticweights in a data structure for use in diagnosis of diseases associatedwith the medical symptom.
 14. The method defined in claim 13, whereinthe aspects are identified from a group comprising quality, severity,location, size, symmetry, timing, localizability, and migration.
 15. Acomputerized medical diagnosis system, comprising: a computerizeddevice; a data structure for a spectrum of terms representative of asubjective description for each of a plurality of selected aspects of amedical symptom, wherein each aspect has a different spectrum of termsand wherein each term of the spectrum has a corresponding diagnosticweight; and a diagnosis program configured, during a diagnosticconsultation, to: a) present a spectrum of terms for one aspect of themedical symptom to a patient, b) receive a selected term from among thespectrum of terms, c) apply a weight from the data structurecorresponding to the selected term to a diagnostic score so as todiagnose a medical condition, d) repeat a)-c) for other aspects of themedical symptom so as to receive other selected terms, and e) encode theselected terms into a code.
 16. The system defined in claim 15, whereinthe diagnosis program is further configured to: repeat a)-e) at apredetermined later time; analyze a change of the code over time; assigna code change weight for the change of the code for the medical symptomover time; and apply the code change weight to the diagnostic score. 17.The system defined in claim 16, wherein the predetermined later time islater in the diagnostic consultation.
 18. The system defined in claim16, wherein the predetermined later time is during a subsequentdiagnostic consultation.
 19. The system defined in claim 16, wherein thediagnosis program is further configured to store the code over time in apatient medical history.
 20. The system defined in claim 15, wherein theaspects are selected from a group comprising quality, severity,location, size, symmetry, timing, localizability, and migration.